feat: 新增插件系统、多种插件类型、开发指南及多语言文档。
This commit is contained in:
fujie
527
plugins/actions/summary/summary.py
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527
plugins/actions/summary/summary.py
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"""
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title: Deep Reading & Summary
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author: Antigravity
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author_url: https://github.com/open-webui
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funding_url: https://github.com/open-webui
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version: 0.1.0
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icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwYXRoIGQ9Ik0yIDNIMGEyIDIgMCAwIDAgMiAyIi8+PHBhdGggZD0iTTIyIDNIMjBhMiAyIDAgMCAwLTIgMiIvPjxwYXRoIGQ9Ik0yIDdoMjB2MTRhMiAyIDAgMCAxLTIgMmgtMTZhMiAyIDAgMCAxLTItMnYtMTQiLz48cGF0aCBkPSJNMTEgMTJ2NiIvPjxwYXRoIGQ9Ik0xNiAxMnY2Ii8+PHBhdGggZD0iTTYgMTJ2NiIvPjwvc3ZnPg==
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description: Provides deep reading analysis and summarization for long texts.
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requirements: jinja2, markdown
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"""
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from pydantic import BaseModel, Field
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from typing import Optional, Dict, Any
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import logging
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import re
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from fastapi import Request
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from datetime import datetime
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import pytz
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import markdown
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from jinja2 import Template
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from open_webui.utils.chat import generate_chat_completion
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from open_webui.models.users import Users
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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logger = logging.getLogger(__name__)
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# =================================================================
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# 内部 LLM 提示词设计
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# =================================================================
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SYSTEM_PROMPT_READING_ASSISTANT = """
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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。
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请提供以下内容：
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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。
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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：
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- 具体且有深度
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- 包含必要的细节和背景
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- 使用 Markdown 列表格式
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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：
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- 明确且可操作
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- 包含执行的优先级或时间建议
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- 如果没有明确的行动项，可以提供学习建议或思考方向
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请严格遵循以下指导原则：
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- **语言**：所有输出必须使用用户指定的语言。
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- **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：
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## 摘要
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[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]
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## 关键信息点
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- [关键点1：包含具体细节和背景]
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- [关键点2：包含具体细节和背景]
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- [关键点3：包含具体细节和背景]
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- [至少5个，最多8个关键点]
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## 行动建议
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- [行动项1：具体、可执行，包含优先级]
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- [行动项2：具体、可执行，包含优先级]
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- [如果没有明确行动项，提供学习建议或思考方向]
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- **深度优先**：分析要深入、全面，不要浮于表面。
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- **行动导向**：重点关注可执行的建议和下一步行动。
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- **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。
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"""
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USER_PROMPT_GENERATE_SUMMARY = """
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请对以下长篇文本进行深度分析，提供：
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1. 详细的摘要（2-3段话，全面概括文本内容）
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2. 关键信息点列表（5-8个，包含具体细节）
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3. 可执行的行动建议（具体、明确，包含优先级）
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---
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**用户上下文信息:**
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用户姓名: {user_name}
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当前日期时间: {current_date_time_str}
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当前星期: {current_weekday}
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当前时区: {current_timezone_str}
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用户语言: {user_language}
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---
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**长篇文本内容:**
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```
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{long_text_content}
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```
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请进行深入、全面的分析，重点关注可执行的行动建议。
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"""
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# =================================================================
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# 前端 HTML 模板 (Jinja2 语法)
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# =================================================================
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HTML_TEMPLATE = """
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<!DOCTYPE html>
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<html lang="{{ user_language }}">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>精读:深度分析报告</title>
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<style>
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:root {
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--primary-color: #4285f4;
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--secondary-color: #1e88e5;
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--action-color: #34a853;
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--background-color: #f8f9fa;
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--card-bg-color: #ffffff;
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--text-color: #202124;
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--muted-text-color: #5f6368;
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--border-color: #dadce0;
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--header-gradient: linear-gradient(135deg, #4285f4, #1e88e5);
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--shadow: 0 1px 3px rgba(60,64,67,.3);
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--border-radius: 8px;
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--font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
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}
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body {
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font-family: var(--font-family);
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line-height: 1.8;
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color: var(--text-color);
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margin: 0;
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padding: 24px;
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background-color: var(--background-color);
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-webkit-font-smoothing: antialiased;
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-moz-osx-font-smoothing: grayscale;
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}
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.container {
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max-width: 900px;
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margin: 20px auto;
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background: var(--card-bg-color);
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border-radius: var(--border-radius);
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box-shadow: var(--shadow);
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overflow: hidden;
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border: 1px solid var(--border-color);
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}
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.header {
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background: var(--header-gradient);
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color: white;
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padding: 40px;
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text-align: center;
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}
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.header h1 {
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margin: 0;
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font-size: 2.2em;
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font-weight: 500;
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letter-spacing: -0.5px;
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}
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.user-context {
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font-size: 0.9em;
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color: var(--muted-text-color);
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background-color: #f1f3f4;
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padding: 16px 40px;
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display: flex;
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justify-content: space-around;
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flex-wrap: wrap;
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border-bottom: 1px solid var(--border-color);
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}
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.user-context span { margin: 4px 12px; }
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.content { padding: 40px; }
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.section {
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margin-bottom: 32px;
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padding-bottom: 32px;
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border-bottom: 1px solid #e8eaed;
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}
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.section:last-child {
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border-bottom: none;
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margin-bottom: 0;
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padding-bottom: 0;
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}
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.section h2 {
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margin-top: 0;
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margin-bottom: 20px;
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font-size: 1.5em;
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font-weight: 500;
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color: var(--text-color);
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display: flex;
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align-items: center;
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padding-bottom: 12px;
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border-bottom: 2px solid var(--primary-color);
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}
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.section h2 .icon {
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margin-right: 12px;
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font-size: 1.3em;
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line-height: 1;
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}
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.summary-section h2 { border-bottom-color: var(--primary-color); }
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.keypoints-section h2 { border-bottom-color: var(--secondary-color); }
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.actions-section h2 { border-bottom-color: var(--action-color); }
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.html-content {
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font-size: 1.05em;
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line-height: 1.8;
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}
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.html-content p:first-child { margin-top: 0; }
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.html-content p:last-child { margin-bottom: 0; }
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.html-content ul {
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list-style: none;
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padding-left: 0;
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margin: 16px 0;
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}
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.html-content li {
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padding: 12px 0 12px 32px;
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position: relative;
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margin-bottom: 8px;
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line-height: 1.7;
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}
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.html-content li::before {
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position: absolute;
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left: 0;
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top: 12px;
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font-family: 'Arial';
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font-weight: bold;
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font-size: 1.1em;
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}
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.keypoints-section .html-content li::before {
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content: '•';
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color: var(--secondary-color);
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font-size: 1.5em;
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top: 8px;
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}
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.actions-section .html-content li::before {
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content: '▸';
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color: var(--action-color);
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}
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.no-content {
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color: var(--muted-text-color);
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font-style: italic;
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padding: 20px;
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background: #f8f9fa;
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border-radius: 4px;
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}
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.footer {
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text-align: center;
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padding: 24px;
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font-size: 0.85em;
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color: #5f6368;
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background-color: #f8f9fa;
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border-top: 1px solid var(--border-color);
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}
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</style>
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</head>
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<body>
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<div class="container">
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<div class="header">
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<h1>📖 精读：深度分析报告</h1>
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</div>
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<div class="user-context">
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<span><strong>用户:</strong> {{ user_name }}</span>
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<span><strong>分析时间:</strong> {{ current_date_time_str }}</span>
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<span><strong>星期:</strong> {{ current_weekday }}</span>
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</div>
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<div class="content">
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<div class="section summary-section">
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<h2><span class="icon">📝</span>详细摘要</h2>
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<div class="html-content">{{ summary_html | safe }}</div>
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</div>
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<div class="section keypoints-section">
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<h2><span class="icon">💡</span>关键信息点</h2>
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<div class="html-content">{{ keypoints_html | safe }}</div>
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</div>
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<div class="section actions-section">
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<h2><span class="icon">🎯</span>行动建议</h2>
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<div class="html-content">{{ actions_html | safe }}</div>
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</div>
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</div>
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<div class="footer">
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<p>© {{ current_year }} 精读 - 深度文本分析服务</p>
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</div>
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</div>
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</body>
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</html>"""
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class Action:
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class Valves(BaseModel):
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show_status: bool = Field(
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default=True, description="是否在聊天界面显示操作状态更新。"
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)
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LLM_MODEL_ID: str = Field(
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default="gemini-2.5-flash",
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description="用于文本分析的内置LLM模型ID。",
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)
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MIN_TEXT_LENGTH: int = Field(
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default=200,
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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。",
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)
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RECOMMENDED_MIN_LENGTH: int = Field(
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default=500, description="建议的最小文本长度，以获得最佳分析效果。"
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)
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def __init__(self):
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self.valves = self.Valves()
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def _process_llm_output(self, llm_output: str) -> Dict[str, str]:
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"""
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解析LLM的Markdown输出,将其转换为HTML片段。
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"""
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summary_match = re.search(
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r"##\s*摘要\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL
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)
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keypoints_match = re.search(
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r"##\s*关键信息点\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL
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)
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actions_match = re.search(
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r"##\s*行动建议\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL
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)
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summary_md = summary_match.group(1).strip() if summary_match else ""
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keypoints_md = keypoints_match.group(1).strip() if keypoints_match else ""
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actions_md = actions_match.group(1).strip() if actions_match else ""
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if not any([summary_md, keypoints_md, actions_md]):
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summary_md = llm_output.strip()
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logger.warning("LLM输出未遵循预期的Markdown格式。将整个输出视为摘要。")
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# 使用 'nl2br' 扩展将换行符 \n 转换为 <br>
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md_extensions = ["nl2br"]
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summary_html = (
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markdown.markdown(summary_md, extensions=md_extensions)
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if summary_md
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else '<p class="no-content">未能提取摘要信息。</p>'
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)
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keypoints_html = (
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markdown.markdown(keypoints_md, extensions=md_extensions)
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if keypoints_md
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else '<p class="no-content">未能提取关键信息点。</p>'
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)
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actions_html = (
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markdown.markdown(actions_md, extensions=md_extensions)
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if actions_md
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else '<p class="no-content">暂无明确的行动建议。</p>'
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)
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return {
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"summary_html": summary_html,
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"keypoints_html": keypoints_html,
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"actions_html": actions_html,
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}
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def _build_html(self, context: dict) -> str:
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"""
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使用 Jinja2 模板和上下文数据构建最终的HTML内容。
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"""
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template = Template(HTML_TEMPLATE)
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return template.render(context)
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async def action(
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self,
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body: dict,
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__user__: Optional[Dict[str, Any]] = None,
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__event_emitter__: Optional[Any] = None,
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__request__: Optional[Request] = None,
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) -> Optional[dict]:
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logger.info("Action: 精读启动 (v2.0.0 - Deep Reading)")
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if isinstance(__user__, (list, tuple)):
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user_language = (
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__user__[0].get("language", "zh-CN") if __user__ else "zh-CN"
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)
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user_name = __user__[0].get("name", "用户") if __user__[0] else "用户"
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user_id = (
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__user__[0]["id"]
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if __user__ and "id" in __user__[0]
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else "unknown_user"
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)
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elif isinstance(__user__, dict):
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user_language = __user__.get("language", "zh-CN")
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user_name = __user__.get("name", "用户")
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user_id = __user__.get("id", "unknown_user")
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now = datetime.now()
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current_date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
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current_weekday = now.strftime("%A")
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current_year = now.strftime("%Y")
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current_timezone_str = "未知时区"
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original_content = ""
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try:
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messages = body.get("messages", [])
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if not messages or not messages[-1].get("content"):
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raise ValueError("无法获取有效的用户消息内容。")
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original_content = messages[-1]["content"]
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if len(original_content) < self.valves.MIN_TEXT_LENGTH:
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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。"
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if __event_emitter__:
|
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await __event_emitter__(
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{
|
||||
"type": "notification",
|
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"data": {"type": "warning", "content": short_text_message},
|
||||
}
|
||||
)
|
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return {
|
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"messages": [
|
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{"role": "assistant", "content": f"⚠️ {short_text_message}"}
|
||||
]
|
||||
}
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# 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
|
||||
Reference in New Issue
Block a user