feat: 添加了中英文动作插件模板，更新了摘要和智能思维导图插件，并简化了异步上下文压缩插件的模型阈值配置。
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fujie
274
plugins/actions/ACTION_PLUGIN_TEMPLATE.py
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274
plugins/actions/ACTION_PLUGIN_TEMPLATE.py
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"""
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title: [Plugin Name] (e.g., Smart Mind Map)
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author: [Your Name]
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author_url: [Your URL]
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funding_url: [Funding URL]
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version: 0.1.0
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icon_url: [Data URI or URL for Icon]
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description: [Brief description of what the plugin does]
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requirements: [List of dependencies, e.g., 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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# Import OpenWebUI utilities
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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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# Setup logging
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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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# Constants & Prompts
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# =================================================================
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SYSTEM_PROMPT = """
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[Insert System Prompt Here]
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You are a helpful assistant...
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Please output in [JSON/Markdown] format...
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"""
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USER_PROMPT_TEMPLATE = """
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[Insert User Prompt Template Here]
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User Context:
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Name: {user_name}
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Time: {current_date_time_str}
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Content to process:
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{content}
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"""
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# HTML Template for rendering the result in the chat
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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>[Plugin Title]</title>
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<style>
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/* Add your CSS styles here */
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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>[Result Title]</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="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 processing. 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=50,
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description="Minimum text length required for processing (characters).",
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)
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# Add other configuration fields as needed
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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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"""Extracts user context information."""
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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", "User"),
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"user_language": user_data.get("language", "en-US"),
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}
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def _get_current_time_context(self) -> Dict[str, str]:
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"""Gets current time context."""
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try:
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# Default to a specific timezone or system time
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tz = pytz.timezone("Asia/Shanghai") # Change as needed
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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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Process the raw output from the LLM.
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Override this method to parse JSON, extract Markdown, etc.
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"""
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# Example: Extract 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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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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"""Emits a status update event."""
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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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"""Emits a notification event (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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"""Emits a message event (appends to current message)."""
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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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"""Emits a replace event (replaces current message)."""
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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. Context Setup
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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. Input Validation
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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 # Or handle error
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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"Text too short ({len(original_content)} chars). Minimum required: {self.valves.MIN_TEXT_LENGTH}."
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await self._emit_notification(__event_emitter__, warning_msg, "warning")
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return body # Or return a message indicating failure
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# 3. Status Notification (Start)
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await self._emit_status(__event_emitter__, "Processing...", done=False)
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try:
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# 4. Prepare Prompt
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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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# Add other context variables
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)
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# 5. Determine Model
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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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# Note: No hardcoded fallback here, relies on system/user context
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# 6. Call 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("Invalid LLM response")
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assistant_content = llm_response["choices"][0]["message"]["content"]
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# 7. Process Output
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processed_data = self._process_llm_output(assistant_content)
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# 8. Generate HTML/Result
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# Example: simple string replacement
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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. Inject Result
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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. Status Notification (Success)
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await self._emit_status(
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__event_emitter__, "Completed successfully!", done=True
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)
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await self._emit_notification(
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__event_emitter__, "Action completed successfully.", "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"Error: {str(e)}"
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# Append error to chat (optional)
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body["messages"][-1]["content"] += f"\n\n❌ **Error**: {error_msg}"
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return body
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