664 lines
25 KiB
Python
664 lines
25 KiB
Python
"""
|
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title: Deep Reading & Summary
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author: Fu-Jie
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author_url: https://github.com/Fu-Jie
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funding_url: https://github.com/Fu-Jie/awesome-openwebui
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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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# HTML Wrapper Template (supports multiple plugins and grid layout)
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# =================================================================
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HTML_WRAPPER_TEMPLATE = """
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<!-- OPENWEBUI_PLUGIN_OUTPUT -->
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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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<style>
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body {
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font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
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margin: 0;
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padding: 10px;
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background-color: transparent;
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}
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#main-container {
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display: flex;
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flex-wrap: wrap;
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gap: 20px;
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align-items: flex-start;
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width: 100%;
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}
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.plugin-item {
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flex: 1 1 400px; /* Default width, allows shrinking/growing */
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min-width: 300px;
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background: white;
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border-radius: 12px;
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box-shadow: 0 4px 6px rgba(0,0,0,0.05);
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overflow: hidden;
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border: 1px solid #e5e7eb;
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transition: all 0.3s ease;
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}
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.plugin-item:hover {
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box-shadow: 0 10px 15px rgba(0,0,0,0.1);
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}
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@media (max-width: 768px) {
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.plugin-item { flex: 1 1 100%; }
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}
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/* STYLES_INSERTION_POINT */
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</style>
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</head>
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<body>
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<div id="main-container">
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<!-- CONTENT_INSERTION_POINT -->
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</div>
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<!-- SCRIPTS_INSERTION_POINT -->
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</body>
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</html>
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"""
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# =================================================================
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# Internal LLM Prompts
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# =================================================================
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SYSTEM_PROMPT_READING_ASSISTANT = """
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You are a professional Deep Text Analysis Expert, specializing in reading long texts and extracting the essence. Your task is to conduct a comprehensive and in-depth analysis.
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Please provide the following:
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1. **Detailed Summary**: Summarize the core content of the text in 2-3 paragraphs, ensuring accuracy and completeness. Do not be too brief; ensure the reader fully understands the main idea.
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2. **Key Information Points**: List 5-8 most important facts, viewpoints, or arguments. Each point should:
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- Be specific and insightful
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- Include necessary details and context
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- Use Markdown list format
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3. **Actionable Advice**: Identify and refine specific, actionable items from the text. Each suggestion should:
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- Be clear and actionable
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- Include execution priority or timing suggestions
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- If there are no clear action items, provide learning suggestions or thinking directions
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Please strictly follow these guidelines:
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- **Language**: All output must be in the user's specified language.
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- **Format**: Please strictly follow the Markdown format below, ensuring each section has a clear header:
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## Summary
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[Detailed summary content here, 2-3 paragraphs, use Markdown **bold** or *italic* to emphasize key points]
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## Key Information Points
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- [Key Point 1: Include specific details and context]
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- [Key Point 2: Include specific details and context]
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- [Key Point 3: Include specific details and context]
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- [At least 5, at most 8 key points]
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## Actionable Advice
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- [Action Item 1: Specific, actionable, include priority]
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- [Action Item 2: Specific, actionable, include priority]
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- [If no clear action items, provide learning suggestions or thinking directions]
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- **Depth First**: Analysis should be deep and comprehensive, not superficial.
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- **Action Oriented**: Focus on actionable suggestions and next steps.
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- **Analysis Results Only**: Do not include any extra pleasantries, explanations, or leading text.
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"""
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USER_PROMPT_GENERATE_SUMMARY = """
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Please conduct a deep analysis of the following long text, providing:
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1. Detailed Summary (2-3 paragraphs, comprehensive overview)
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2. Key Information Points List (5-8 items, including specific details)
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3. Actionable Advice (Specific, clear, including priority)
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---
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**User Context:**
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User Name: {user_name}
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Current Date/Time: {current_date_time_str}
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Weekday: {current_weekday}
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Timezone: {current_timezone_str}
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User Language: {user_language}
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---
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**Long Text Content:**
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```
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{long_text_content}
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```
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Please conduct a deep and comprehensive analysis, focusing on actionable advice.
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"""
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# =================================================================
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# Frontend HTML Template (Jinja2 Syntax)
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# =================================================================
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CSS_TEMPLATE_SUMMARY = """
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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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.summary-container-wrapper {
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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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height: 100%;
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display: flex;
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flex-direction: column;
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}
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.summary-container-wrapper .header {
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background: var(--header-gradient);
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color: white;
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padding: 20px 24px;
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text-align: center;
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}
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.summary-container-wrapper .header h1 {
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margin: 0;
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font-size: 1.5em;
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font-weight: 500;
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letter-spacing: -0.5px;
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}
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.summary-container-wrapper .user-context {
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font-size: 0.8em;
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color: var(--muted-text-color);
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background-color: #f1f3f4;
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padding: 8px 16px;
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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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.summary-container-wrapper .user-context span { margin: 2px 8px; }
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.summary-container-wrapper .content { padding: 20px; flex-grow: 1; }
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.summary-container-wrapper .section {
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margin-bottom: 16px;
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padding-bottom: 16px;
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border-bottom: 1px solid #e8eaed;
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}
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.summary-container-wrapper .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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.summary-container-wrapper .section h2 {
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margin-top: 0;
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margin-bottom: 12px;
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font-size: 1.2em;
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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: 8px;
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border-bottom: 2px solid var(--primary-color);
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}
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.summary-container-wrapper .section h2 .icon {
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margin-right: 8px;
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font-size: 1.1em;
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line-height: 1;
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}
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.summary-container-wrapper .summary-section h2 { border-bottom-color: var(--primary-color); }
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.summary-container-wrapper .keypoints-section h2 { border-bottom-color: var(--secondary-color); }
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.summary-container-wrapper .actions-section h2 { border-bottom-color: var(--action-color); }
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.summary-container-wrapper .html-content {
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font-size: 0.95em;
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line-height: 1.7;
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}
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.summary-container-wrapper .html-content p:first-child { margin-top: 0; }
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.summary-container-wrapper .html-content p:last-child { margin-bottom: 0; }
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.summary-container-wrapper .html-content ul {
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list-style: none;
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padding-left: 0;
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margin: 12px 0;
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}
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.summary-container-wrapper .html-content li {
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padding: 8px 0 8px 24px;
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position: relative;
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margin-bottom: 6px;
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line-height: 1.6;
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}
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.summary-container-wrapper .html-content li::before {
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position: absolute;
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left: 0;
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top: 8px;
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font-family: 'Arial';
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font-weight: bold;
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font-size: 1em;
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}
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.summary-container-wrapper .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: 1.3em;
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top: 5px;
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}
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.summary-container-wrapper .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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.summary-container-wrapper .no-content {
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color: var(--muted-text-color);
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font-style: italic;
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padding: 12px;
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background: #f8f9fa;
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border-radius: 4px;
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}
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.summary-container-wrapper .footer {
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text-align: center;
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padding: 16px;
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font-size: 0.8em;
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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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"""
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CONTENT_TEMPLATE_SUMMARY = """
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<div class="summary-container-wrapper">
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<div class="header">
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<h1>📖 Deep Reading: Analysis Report</h1>
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</div>
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<div class="user-context">
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<span><strong>User:</strong> {user_name}</span>
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<span><strong>Time:</strong> {current_date_time_str}</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>Detailed Summary</h2>
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<div class="html-content">{summary_html}</div>
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</div>
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<div class="section keypoints-section">
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<h2><span class="icon">💡</span>Key Information Points</h2>
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<div class="html-content">{keypoints_html}</div>
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</div>
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<div class="section actions-section">
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<h2><span class="icon">🎯</span>Actionable Advice</h2>
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<div class="html-content">{actions_html}</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} Deep Reading - Text Analysis Service</p>
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</div>
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</div>
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"""
|
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|
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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,
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description="Whether to show operation status updates in the chat interface.",
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)
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LLM_MODEL_ID: str = Field(
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default="",
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description="Built-in LLM Model ID used for text analysis. If empty, uses the current conversation's model.",
|
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)
|
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MIN_TEXT_LENGTH: int = Field(
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default=200,
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description="Minimum text length required for deep analysis (characters). Recommended 200+.",
|
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)
|
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RECOMMENDED_MIN_LENGTH: int = Field(
|
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default=500,
|
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description="Recommended minimum text length for best analysis results.",
|
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)
|
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CLEAR_PREVIOUS_HTML: bool = Field(
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default=False,
|
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description="Whether to force clear previous plugin results (if True, overwrites instead of merging).",
|
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)
|
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|
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def __init__(self):
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self.valves = self.Valves()
|
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|
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def _process_llm_output(self, llm_output: str) -> Dict[str, str]:
|
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"""
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Parse LLM Markdown output and convert to HTML fragments.
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"""
|
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summary_match = re.search(
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r"##\s*Summary\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL | re.IGNORECASE
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)
|
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keypoints_match = re.search(
|
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r"##\s*Key Information Points\s*\n(.*?)(?=\n##|$)",
|
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llm_output,
|
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re.DOTALL | re.IGNORECASE,
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)
|
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actions_match = re.search(
|
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r"##\s*Actionable Advice\s*\n(.*?)(?=\n##|$)",
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llm_output,
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re.DOTALL | re.IGNORECASE,
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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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|
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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(
|
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"LLM output did not follow expected Markdown format. Treating entire output as summary."
|
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)
|
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|
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# Use 'nl2br' extension to convert newlines \n to <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">Failed to extract summary.</p>'
|
|
)
|
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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">Failed to extract key information points.</p>'
|
|
)
|
|
actions_html = (
|
|
markdown.markdown(actions_md, extensions=md_extensions)
|
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if actions_md
|
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else '<p class="no-content">No explicit actionable advice.</p>'
|
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)
|
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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,
|
|
"actions_html": actions_html,
|
|
}
|
|
|
|
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]*?```"
|
|
return re.sub(pattern, "", content).strip()
|
|
|
|
def _merge_html(
|
|
self,
|
|
existing_html_code: str,
|
|
new_content: str,
|
|
new_styles: str = "",
|
|
new_scripts: str = "",
|
|
user_language: str = "en-US",
|
|
) -> str:
|
|
"""
|
|
Merges new content into an existing HTML container, or creates a new one.
|
|
"""
|
|
if (
|
|
"<!-- OPENWEBUI_PLUGIN_OUTPUT -->" in existing_html_code
|
|
and "<!-- CONTENT_INSERTION_POINT -->" in existing_html_code
|
|
):
|
|
base_html = existing_html_code
|
|
base_html = re.sub(r"^```html\s*", "", base_html)
|
|
base_html = re.sub(r"\s*```$", "", base_html)
|
|
else:
|
|
base_html = HTML_WRAPPER_TEMPLATE.replace("{user_language}", user_language)
|
|
|
|
wrapped_content = f'<div class="plugin-item">\n{new_content}\n</div>'
|
|
|
|
if new_styles:
|
|
base_html = base_html.replace(
|
|
"/* STYLES_INSERTION_POINT */",
|
|
f"{new_styles}\n/* STYLES_INSERTION_POINT */",
|
|
)
|
|
|
|
base_html = base_html.replace(
|
|
"<!-- CONTENT_INSERTION_POINT -->",
|
|
f"{wrapped_content}\n<!-- CONTENT_INSERTION_POINT -->",
|
|
)
|
|
|
|
if new_scripts:
|
|
base_html = base_html.replace(
|
|
"<!-- SCRIPTS_INSERTION_POINT -->",
|
|
f"{new_scripts}\n<!-- SCRIPTS_INSERTION_POINT -->",
|
|
)
|
|
|
|
return base_html.strip()
|
|
|
|
def _build_content_html(self, context: dict) -> str:
|
|
"""
|
|
Build content HTML using context data.
|
|
"""
|
|
return (
|
|
CONTENT_TEMPLATE_SUMMARY.replace(
|
|
"{user_name}", context.get("user_name", "User")
|
|
)
|
|
.replace(
|
|
"{current_date_time_str}", context.get("current_date_time_str", "")
|
|
)
|
|
.replace("{current_year}", context.get("current_year", ""))
|
|
.replace("{summary_html}", context.get("summary_html", ""))
|
|
.replace("{keypoints_html}", context.get("keypoints_html", ""))
|
|
.replace("{actions_html}", context.get("actions_html", ""))
|
|
)
|
|
|
|
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: Deep Reading Started (v2.0.0)")
|
|
|
|
if isinstance(__user__, (list, tuple)):
|
|
user_language = (
|
|
__user__[0].get("language", "en-US") if __user__ else "en-US"
|
|
)
|
|
user_name = __user__[0].get("name", "User") if __user__[0] else "User"
|
|
user_id = (
|
|
__user__[0]["id"]
|
|
if __user__ and "id" in __user__[0]
|
|
else "unknown_user"
|
|
)
|
|
elif isinstance(__user__, dict):
|
|
user_language = __user__.get("language", "en-US")
|
|
user_name = __user__.get("name", "User")
|
|
user_id = __user__.get("id", "unknown_user")
|
|
|
|
now = datetime.now()
|
|
current_date_time_str = now.strftime("%B %d, %Y %H:%M:%S")
|
|
current_weekday = now.strftime("%A")
|
|
current_year = now.strftime("%Y")
|
|
current_timezone_str = "Unknown Timezone"
|
|
|
|
original_content = ""
|
|
try:
|
|
messages = body.get("messages", [])
|
|
if not messages or not messages[-1].get("content"):
|
|
raise ValueError("Unable to get valid user message content.")
|
|
|
|
original_content = messages[-1]["content"]
|
|
|
|
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},
|
|
}
|
|
)
|
|
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"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...",
|
|
},
|
|
}
|
|
)
|
|
if self.valves.show_status:
|
|
await __event_emitter__(
|
|
{
|
|
"type": "status",
|
|
"data": {
|
|
"description": "📖 Deep Reading: Analyzing text, extracting essence...",
|
|
"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,
|
|
)
|
|
|
|
# Determine model to use
|
|
target_model = self.valves.LLM_MODEL_ID
|
|
if not target_model:
|
|
target_model = body.get("model")
|
|
|
|
llm_payload = {
|
|
"model": target_model,
|
|
"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"Unable to get user object, User 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,
|
|
}
|
|
|
|
content_html = self._build_content_html(context)
|
|
|
|
# Extract existing HTML if any
|
|
existing_html_block = ""
|
|
match = re.search(
|
|
r"```html\s*(<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?)```",
|
|
original_content,
|
|
)
|
|
if match:
|
|
existing_html_block = match.group(1)
|
|
|
|
if self.valves.CLEAR_PREVIOUS_HTML:
|
|
original_content = self._remove_existing_html(original_content)
|
|
final_html = self._merge_html(
|
|
"", content_html, CSS_TEMPLATE_SUMMARY, "", user_language
|
|
)
|
|
else:
|
|
if existing_html_block:
|
|
original_content = self._remove_existing_html(original_content)
|
|
final_html = self._merge_html(
|
|
existing_html_block,
|
|
content_html,
|
|
CSS_TEMPLATE_SUMMARY,
|
|
"",
|
|
user_language,
|
|
)
|
|
else:
|
|
final_html = self._merge_html(
|
|
"", content_html, CSS_TEMPLATE_SUMMARY, "", user_language
|
|
)
|
|
|
|
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.",
|
|
},
|
|
}
|
|
)
|
|
|
|
except Exception as e:
|
|
error_message = f"Deep Reading processing failed: {str(e)}"
|
|
logger.error(f"Deep Reading Error: {error_message}", exc_info=True)
|
|
user_facing_error = f"Sorry, Deep Reading encountered an error while processing: {str(e)}.\nPlease check Open WebUI backend logs for more details."
|
|
body["messages"][-1][
|
|
"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}!",
|
|
},
|
|
}
|
|
)
|
|
|
|
return body
|