""" title: 闪记卡 (Flash Card) author: Fu-Jie author_url: https://github.com/Fu-Jie funding_url: https://github.com/Fu-Jie/awesome-openwebui version: 0.2.4 openwebui_id: 4a31eac3-a3c4-4c30-9ca5-dab36b5fac65 icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwb2x5Z29uIHBvaW50cz0iMTIgMiAyIDcgMTIgMTIgMjIgNyAxMiAyIi8+PHBvbHlsaW5lIHBvaW50cz0iMiAxNyAxMiAyMiAyMiAxNyIvPjxwb2x5bGluZSBwb2ludHM9IjIgMTIgMTIgMTcgMjIgMTIiLz48L3N2Zz4= description: 快速将文本提炼为精美的学习记忆卡片，支持核心要点提取与分类。 """ from pydantic import BaseModel, Field from typing import Optional, Dict, Any, List import json import logging import re from open_webui.utils.chat import generate_chat_completion from open_webui.models.users import Users # Setup logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) HTML_WRAPPER_TEMPLATE = """

""" class Action: class Valves(BaseModel): MODEL_ID: str = Field( default="", description="用于生成卡片内容的模型 ID。如果为空，则使用当前模型。", ) MIN_TEXT_LENGTH: int = Field( default=50, description="生成闪记卡所需的最小文本长度（字符数）。" ) LANGUAGE: str = Field( default="zh", description="卡片内容的目标语言 (例如 'zh', 'en')。" ) SHOW_STATUS: bool = Field( default=True, description="是否在聊天界面显示状态更新。" ) CLEAR_PREVIOUS_HTML: bool = Field( default=False, description="是否强制清除旧的插件结果（如果为 True，则不合并，直接覆盖）。", ) MESSAGE_COUNT: int = Field( default=1, description="用于生成的最近消息数量。设置为1仅使用最后一条消息，更大值可包含更多上下文。", ) def __init__(self): self.valves = self.Valves() def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]: """安全提取用户上下文信息，支持多种输入格式。""" if isinstance(__user__, (list, tuple)): user_data = __user__[0] if __user__ else {} elif isinstance(__user__, dict): user_data = __user__ else: user_data = {} return { "user_id": user_data.get("id", "unknown_user"), "user_name": user_data.get("name", "用户"), "user_language": user_data.get("language", "zh-CN"), } async def action( self, body: dict, __user__: Optional[Dict[str, Any]] = None, __event_emitter__: Optional[Any] = None, __request__: Optional[Any] = None, ) -> Optional[dict]: logger.info(f"Action: {__name__} 触发") if not __event_emitter__: return body # Get messages based on MESSAGE_COUNT messages = body.get("messages", []) if not messages: return body # Get last N messages based on MESSAGE_COUNT message_count = min(self.valves.MESSAGE_COUNT, len(messages)) recent_messages = messages[-message_count:] # Aggregate content from selected messages with labels aggregated_parts = [] for i, msg in enumerate(recent_messages, 1): text_content = self._extract_text_content(msg.get("content")) if text_content: role = msg.get("role", "unknown") role_label = ( "用户" if role == "user" else "助手" if role == "assistant" else role ) aggregated_parts.append(f"{text_content}") if not aggregated_parts: return body target_message = "\n\n---\n\n".join(aggregated_parts) # Check text length text_length = len(target_message) if text_length < self.valves.MIN_TEXT_LENGTH: await self._emit_notification( __event_emitter__, f"文本内容过短 ({text_length} 字符)，建议至少 {self.valves.MIN_TEXT_LENGTH} 字符。", "warning", ) return body # Notify user that we are generating the card await self._emit_notification(__event_emitter__, "⚡ 正在生成闪记卡...", "info") await self._emit_status(__event_emitter__, "⚡ 闪记卡: 开始生成...", done=False) try: # 1. Extract information using LLM await self._emit_status( __event_emitter__, "⚡ 闪记卡: 正在调用 AI 模型分析内容...", done=False ) user_ctx = self._get_user_context(__user__) user_id = user_ctx["user_id"] user_obj = Users.get_user_by_id(user_id) target_model = ( self.valves.MODEL_ID if self.valves.MODEL_ID else body.get("model") ) system_prompt = f""" 你是一个闪记卡生成专家，专注于创建适合学习和记忆的知识卡片。你的任务是将文本提炼成简洁、易记的学习卡片。请提取以下字段，并以 JSON 格式返回： 1. "title": 创建一个简短、精准的标题（3-8 个词），突出核心概念 2. "summary": 用一句话总结核心要义（10-25 个词），要通俗易懂、便于记忆 3. "key_points": 列出 3-5 个关键记忆点（每个 5-15 个词） - 每个要点应该是独立的知识点 - 使用简洁、口语化的表达 - 避免冗长的句子 4. "tags": 列出 2-4 个分类标签（每个 1-3 个词） 5. "category": 选择一个主分类（如：概念、技能、事实、方法等）目标语言: {self.valves.LANGUAGE} 重要原则： - **极简主义**: 每个要点都要精炼到极致 - **记忆优先**: 内容要便于记忆和回忆 - **核心聚焦**: 只提取最核心的知识点 - **口语化**: 使用通俗易懂的语言 - 只返回 JSON 对象，不要包含 markdown 格式 """ prompt = f"请将以下文本提炼成一张学习记忆卡片：\n\n{target_message}" payload = { "model": target_model, "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}, ], "stream": False, } response = await generate_chat_completion(__request__, payload, user_obj) content = response["choices"][0]["message"]["content"] await self._emit_status( __event_emitter__, "⚡ 闪记卡: AI 分析完成，正在解析数据...", done=False ) # Parse JSON try: # simple cleanup in case of markdown code blocks if "```json" in content: content = content.split("```json")[1].split("```")[0].strip() elif "```" in content: content = content.split("```")[1].split("```")[0].strip() card_data = json.loads(content) except Exception as e: logger.error(f"Failed to parse JSON: {e}, content: {content}") await self._emit_status( __event_emitter__, "❌ 闪记卡: 数据解析失败", done=True ) await self._emit_notification( __event_emitter__, "❌ 生成卡片数据失败，请重试。", "error" ) return body # 2. Generate HTML components await self._emit_status( __event_emitter__, "⚡ 闪记卡: 正在渲染卡片...", done=False ) card_content, card_style = self.generate_html_card_components(card_data) # 3. Append to message # Extract existing HTML if any existing_html_block = "" match = re.search( r"```html\s*([\s\S]*?)```", body["messages"][-1]["content"], ) if match: existing_html_block = match.group(1) if self.valves.CLEAR_PREVIOUS_HTML: body["messages"][-1]["content"] = self._remove_existing_html( body["messages"][-1]["content"] ) final_html = self._merge_html( "", card_content, card_style, "", self.valves.LANGUAGE ) else: if existing_html_block: body["messages"][-1]["content"] = self._remove_existing_html( body["messages"][-1]["content"] ) final_html = self._merge_html( existing_html_block, card_content, card_style, "", self.valves.LANGUAGE, ) else: final_html = self._merge_html( "", card_content, card_style, "", self.valves.LANGUAGE ) html_embed_tag = f"```html\n{final_html}\n```" body["messages"][-1]["content"] += f"\n\n{html_embed_tag}" await self._emit_status( __event_emitter__, "✅ 闪记卡: 生成完成！", done=True ) await self._emit_notification( __event_emitter__, "⚡ 闪记卡生成成功！", "success" ) return body except Exception as e: logger.error(f"Error generating knowledge card: {e}") await self._emit_status(__event_emitter__, "❌ 闪记卡: 生成失败", done=True) await self._emit_notification( __event_emitter__, f"❌ 生成知识卡片时出错: {str(e)}", "error" ) return body async def _emit_status(self, emitter, description: str, done: bool = False): """发送状态更新事件。""" if self.valves.SHOW_STATUS and emitter: await emitter( {"type": "status", "data": {"description": description, "done": done}} ) async def _emit_notification(self, emitter, content: str, ntype: str = "info"): """发送通知事件 (info/success/warning/error)。""" if emitter: await emitter( {"type": "notification", "data": {"type": ntype, "content": content}} ) def _remove_existing_html(self, content: str) -> str: """移除内容中已有的插件生成 HTML 代码块 (通过标记识别)。""" pattern = r"```html\s*[\s\S]*?```" return re.sub(pattern, "", content).strip() def _extract_text_content(self, content) -> str: """从消息内容中提取文本，支持多模态消息格式""" if isinstance(content, str): return content elif isinstance(content, list): # 多模态消息: [{"type": "text", "text": "..."}, {"type": "image_url", ...}] text_parts = [] for item in content: if isinstance(item, dict) and item.get("type") == "text": text_parts.append(item.get("text", "")) elif isinstance(item, str): text_parts.append(item) return "\n".join(text_parts) return str(content) if content else "" def _merge_html( self, existing_html_code: str, new_content: str, new_styles: str = "", new_scripts: str = "", user_language: str = "zh-CN", ) -> str: """ 将新内容合并到现有的 HTML 容器中，或者创建一个新的容器。 """ if ( "" in existing_html_code and "" 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'

\n{new_content}\n

' if new_styles: base_html = base_html.replace( "/* STYLES_INSERTION_POINT */", f"{new_styles}\n/* STYLES_INSERTION_POINT */", ) base_html = base_html.replace( "", f"{wrapped_content}\n", ) if new_scripts: base_html = base_html.replace( "", f"{new_scripts}\n", ) return base_html.strip() def generate_html_card_components(self, data): # Enhanced CSS with premium styling style = """ @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap'); .knowledge-card-container { font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; display: flex; justify-content: center; margin: 30px 0; padding: 0 10px; } .knowledge-card { width: 100%; max-width: 500px; border-radius: 20px; overflow: hidden; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 3px; transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1); position: relative; } .knowledge-card:hover { transform: translateY(-8px) scale(1.02); box-shadow: 0 25px 50px -12px rgba(102, 126, 234, 0.4); } .knowledge-card::before { content: ''; position: absolute; top: -2px; left: -2px; right: -2px; bottom: -2px; background: linear-gradient(135deg, #667eea, #764ba2, #f093fb, #4facfe); border-radius: 20px; opacity: 0; transition: opacity 0.4s ease; z-index: -1; filter: blur(10px); } .knowledge-card:hover::before { opacity: 0.7; animation: glow 2s ease-in-out infinite; } @keyframes glow { 0%, 100% { opacity: 0.5; } 50% { opacity: 0.8; } } .card-inner { background: #ffffff; border-radius: 18px; overflow: hidden; } .card-header { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 32px 28px; position: relative; overflow: hidden; } .card-header::before { content: ''; position: absolute; top: -50%; right: -50%; width: 200%; height: 200%; background: radial-gradient(circle, rgba(255,255,255,0.1) 0%, transparent 70%); animation: rotate 15s linear infinite; } @keyframes rotate { from { transform: rotate(0deg); } to { transform: rotate(360deg); } } .card-category { font-size: 0.7em; text-transform: uppercase; letter-spacing: 2px; opacity: 0.95; margin-bottom: 10px; font-weight: 700; display: inline-block; padding: 4px 12px; background: rgba(255, 255, 255, 0.2); border-radius: 12px; backdrop-filter: blur(10px); } .card-title { font-size: 1.75em; font-weight: 800; margin: 0; line-height: 1.3; position: relative; z-index: 1; letter-spacing: -0.5px; } .card-body { padding: 28px; color: #1a1a1a; background: linear-gradient(to bottom, #ffffff 0%, #fafafa 100%); } .card-summary { font-size: 1.05em; color: #374151; margin-bottom: 24px; line-height: 1.7; border-left: 5px solid #764ba2; padding: 16px 20px; background: linear-gradient(135deg, rgba(102, 126, 234, 0.08) 0%, rgba(118, 75, 162, 0.08) 100%); border-radius: 0 12px 12px 0; font-weight: 500; position: relative; overflow: hidden; } .card-summary::before { content: '"'; position: absolute; top: -10px; left: 10px; font-size: 4em; color: rgba(118, 75, 162, 0.1); font-family: Georgia, serif; font-weight: bold; } .card-section-title { font-size: 0.85em; font-weight: 700; color: #764ba2; margin-bottom: 14px; text-transform: uppercase; letter-spacing: 1.5px; display: flex; align-items: center; gap: 8px; } .card-section-title::before { content: ''; width: 4px; height: 16px; background: linear-gradient(to bottom, #667eea, #764ba2); border-radius: 2px; } .card-points { list-style: none; padding: 0; margin: 0; } .card-points li { margin-bottom: 14px; padding: 12px 16px 12px 44px; position: relative; line-height: 1.6; color: #374151; background: #ffffff; border-radius: 10px; transition: all 0.3s ease; border: 1px solid #e5e7eb; font-weight: 500; } .card-points li:hover { transform: translateX(5px); background: linear-gradient(135deg, rgba(102, 126, 234, 0.05) 0%, rgba(118, 75, 162, 0.05) 100%); border-color: #764ba2; box-shadow: 0 4px 12px rgba(118, 75, 162, 0.1); } .card-points li::before { content: '✓'; color: #ffffff; background: linear-gradient(135deg, #667eea, #764ba2); font-weight: bold; position: absolute; left: 12px; top: 50%; transform: translateY(-50%); width: 24px; height: 24px; display: flex; align-items: center; justify-content: center; border-radius: 50%; font-size: 0.85em; box-shadow: 0 2px 8px rgba(118, 75, 162, 0.3); } .card-footer { padding: 20px 28px; background: linear-gradient(to right, #f8fafc 0%, #f1f5f9 100%); display: flex; flex-wrap: wrap; gap: 10px; border-top: 2px solid #e5e7eb; align-items: center; } .card-tag-label { font-size: 0.75em; font-weight: 700; color: #64748b; text-transform: uppercase; letter-spacing: 1px; margin-right: 4px; } .card-tag { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 6px 16px; border-radius: 20px; font-size: 0.85em; font-weight: 600; transition: all 0.3s ease; border: 2px solid transparent; cursor: default; letter-spacing: 0.3px; } .card-tag:hover { transform: translateY(-2px); box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4); border-color: rgba(255, 255, 255, 0.3); } /* Dark mode support */ @media (prefers-color-scheme: dark) { .card-inner { background: #1e1e1e; } .card-body { background: linear-gradient(to bottom, #1e1e1e 0%, #252525 100%); color: #e5e7eb; } .card-summary { background: linear-gradient(135deg, rgba(102, 126, 234, 0.15) 0%, rgba(118, 75, 162, 0.15) 100%); color: #d1d5db; } .card-summary::before { color: rgba(118, 75, 162, 0.2); } .card-points li { color: #d1d5db; background: #2d2d2d; border-color: #404040; } .card-points li:hover { background: linear-gradient(135deg, rgba(102, 126, 234, 0.15) 0%, rgba(118, 75, 162, 0.15) 100%); border-color: #667eea; } .card-footer { background: linear-gradient(to right, #252525 0%, #2d2d2d 100%); border-top-color: #404040; } .card-tag-label { color: #9ca3af; } } /* Mobile responsive */ @media (max-width: 640px) { .knowledge-card { max-width: 100%; } .card-header { padding: 24px 20px; } .card-title { font-size: 1.5em; } .card-body { padding: 20px; } .card-footer { padding: 16px 20px; } } """ # Generate tags HTML tags_html = "" if "tags" in data and data["tags"]: for tag in data["tags"]: tags_html += f'

#{tag}

' # Generate key points HTML points_html = "" if "key_points" in data and data["key_points"]: for point in data["key_points"]: points_html += f"

{point}

" # Build the card HTML structure content = f"""

{data.get('category', 'Knowledge')}

{data.get('title', 'Flash Card')}

{data.get('summary', '')}

KEY POINTS

{points_html}

{tags_html}

""" return content, style