295 lines
10 KiB
Python
295 lines
10 KiB
Python
"""
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title: [插件名称] (例如: 智能思维导图)
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author: [作者姓名]
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author_url: [作者主页链接]
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funding_url: [赞助链接]
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version: 0.1.0
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icon_url: [图标 URL 或 Data URI]
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description: [简短描述插件的功能]
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requirements: [依赖列表, 例如: 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, List, Callable, Awaitable
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import logging
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import re
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import json
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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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# 导入 OpenWebUI 工具函数
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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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# 设置日志
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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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# 常量与提示词 (Constants & Prompts)
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# =================================================================
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SYSTEM_PROMPT = """
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[在此处插入系统提示词]
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你是一个有用的助手...
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请以 [JSON/Markdown] 格式输出...
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"""
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USER_PROMPT_TEMPLATE = """
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[在此处插入用户提示词模板]
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用户上下文:
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姓名: {user_name}
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时间: {current_date_time_str}
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待处理内容:
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{content}
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"""
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# 用于在聊天中渲染结果的 HTML 模板
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HTML_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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<title>[插件标题]</title>
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<style>
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/* 在此处添加 CSS 样式 */
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body { font-family: sans-serif; padding: 20px; }
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.container { border: 1px solid #ccc; padding: 20px; border-radius: 8px; }
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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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<h1>[结果标题]</h1>
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<div id="content">{result_content}</div>
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</div>
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</body>
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</html>
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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="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="",
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description="用于处理的内置 LLM 模型 ID。如果为空，则使用当前对话的模型。",
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)
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MIN_TEXT_LENGTH: int = Field(
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default=50,
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description="处理所需的最小文本长度（字符数）。",
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)
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CLEAR_PREVIOUS_HTML: bool = Field(
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default=False,
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description="是否在追加新结果前清除消息中已有的插件生成 HTML 内容 (通过标记识别)。",
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)
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# 根据需要添加其他配置字段
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# MAX_TEXT_LENGTH: int = Field(default=2000, description="...")
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def __init__(self):
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self.valves = self.Valves()
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def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
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"""提取用户上下文信息。"""
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if isinstance(__user__, (list, tuple)):
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user_data = __user__[0] if __user__ else {}
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elif isinstance(__user__, dict):
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user_data = __user__
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else:
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user_data = {}
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return {
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"user_id": user_data.get("id", "unknown_user"),
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"user_name": user_data.get("name", "用户"),
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"user_language": user_data.get("language", "zh-CN"),
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}
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def _get_current_time_context(self) -> Dict[str, str]:
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"""获取当前时间上下文。"""
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try:
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# 默认为特定时区或系统时间
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tz = pytz.timezone("Asia/Shanghai") # 根据需要修改
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now = datetime.now(tz)
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except Exception:
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now = datetime.now()
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return {
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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": str(now.tzinfo) if now.tzinfo else "Unknown",
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}
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def _process_llm_output(self, llm_output: str) -> Any:
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"""
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处理 LLM 的原始输出。
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重写此方法以解析 JSON、提取 Markdown 等。
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"""
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# 示例: 提取 JSON
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# try:
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# start = llm_output.find('{')
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# end = llm_output.rfind('}') + 1
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# if start != -1 and end != -1:
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# return json.loads(llm_output[start:end])
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# except Exception:
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# pass
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return llm_output.strip()
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def _remove_existing_html(self, content: str) -> str:
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"""移除内容中已有的插件生成 HTML 代码块 (通过标记识别)。"""
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# 匹配 ```html <!-- OPENWEBUI_PLUGIN_OUTPUT --> ... ``` 模式
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# 使用 [\s\S]*? 非贪婪匹配任意字符
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pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
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return re.sub(pattern, "", content).strip()
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async def _emit_status(
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self,
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emitter: Optional[Callable[[Any], Awaitable[None]]],
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description: str,
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done: bool = False,
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):
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"""发送状态更新事件。"""
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if self.valves.show_status and emitter:
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await emitter(
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{"type": "status", "data": {"description": description, "done": done}}
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)
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async def _emit_notification(
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self,
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emitter: Optional[Callable[[Any], Awaitable[None]]],
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content: str,
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type: str = "info",
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):
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"""发送通知事件 (info, success, warning, error)。"""
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if emitter:
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await emitter(
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{"type": "notification", "data": {"type": type, "content": content}}
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)
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async def _emit_message(
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self, emitter: Optional[Callable[[Any], Awaitable[None]]], content: str
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):
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"""发送消息追加事件 (追加到当前消息)。"""
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if emitter:
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await emitter({"type": "message", "data": {"content": content}})
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async def _emit_replace(
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self, emitter: Optional[Callable[[Any], Awaitable[None]]], content: str
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):
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"""发送消息替换事件 (替换当前消息)。"""
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if emitter:
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await emitter({"type": "replace", "data": {"content": content}})
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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[Callable[[Any], Awaitable[None]]] = None,
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__event_call__: Optional[Callable[[Any], Awaitable[Any]]] = None,
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__request__: Optional[Request] = None,
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) -> Optional[dict]:
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logger.info(f"Action: {__name__} started")
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# 1. 上下文设置
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user_context = self._get_user_context(__user__)
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time_context = self._get_current_time_context()
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# 2. 输入验证
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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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return body # 或者处理错误
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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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warning_msg = f"文本过短 ({len(original_content)} 字符)。最少需要: {self.valves.MIN_TEXT_LENGTH}。"
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await self._emit_notification(__event_emitter__, warning_msg, "warning")
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return body # 或者返回失败消息
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# 3. 状态通知 (开始)
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await self._emit_status(__event_emitter__, "正在处理...", done=False)
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try:
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# 4. 准备提示词
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formatted_prompt = USER_PROMPT_TEMPLATE.format(
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user_name=user_context["user_name"],
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current_date_time_str=time_context["current_date_time_str"],
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content=original_content,
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# 添加其他上下文变量
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)
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# 5. 确定模型
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target_model = self.valves.LLM_MODEL_ID
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if not target_model:
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target_model = body.get("model")
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# 注意: 这里没有硬编码的回退，依赖于系统/用户上下文
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# 6. 调用 LLM
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user_obj = Users.get_user_by_id(user_context["user_id"])
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payload = {
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"model": target_model,
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"messages": [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": formatted_prompt},
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],
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"stream": False,
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# "temperature": 0.5,
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}
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llm_response = await generate_chat_completion(
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__request__, payload, user_obj
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)
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if not llm_response or "choices" not in llm_response:
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raise ValueError("无效的 LLM 响应")
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assistant_content = llm_response["choices"][0]["message"]["content"]
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# 7. 处理输出
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processed_data = self._process_llm_output(assistant_content)
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# 8. 生成 HTML/结果
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# 示例: 简单的字符串替换
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final_html = HTML_TEMPLATE.replace("{result_content}", str(processed_data))
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final_html = final_html.replace(
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"{user_language}", user_context["user_language"]
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)
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# 9. 注入结果
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if self.valves.CLEAR_PREVIOUS_HTML:
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body["messages"][-1]["content"] = self._remove_existing_html(
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body["messages"][-1]["content"]
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)
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html_embed_tag = f"```html\n{final_html}\n```"
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body["messages"][-1]["content"] += f"\n\n{html_embed_tag}"
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# 10. 状态通知 (成功)
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await self._emit_status(__event_emitter__, "处理完成！", done=True)
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await self._emit_notification(
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__event_emitter__, "操作成功完成。", "success"
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)
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except Exception as e:
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logger.error(f"Action failed: {e}", exc_info=True)
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error_msg = f"错误: {str(e)}"
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# 将错误附加到聊天中 (可选)
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body["messages"][-1]["content"] += f"\n\n❌ **错误**: {error_msg}"
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await self._emit_status(__event_emitter__, "处理失败。", done=True)
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await self._emit_notification(
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__event_emitter__, "操作失败，请检查日志。", "error"
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)
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return body
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