"""
title: [Plugin Name] (e.g., Smart Mind Map)
author: [Your Name]
author_url: [Your URL]
funding_url: [Funding URL]
version: 0.1.0
icon_url: [Data URI or URL for Icon]
description: [Brief description of what the plugin does]
requirements: [List of dependencies, e.g., jinja2, markdown]
"""
from pydantic import BaseModel, Field
from typing import Optional, Dict, Any, List, Callable, Awaitable
import logging
import re
import json
from fastapi import Request
from datetime import datetime
import pytz
# Import OpenWebUI utilities
from open_webui.utils.chat import generate_chat_completion
from open_webui.models.users import Users
# Setup logging
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
# =================================================================
# Constants & Prompts
# =================================================================
SYSTEM_PROMPT = """
[Insert System Prompt Here]
You are a helpful assistant...
Please output in [JSON/Markdown] format...
"""
USER_PROMPT_TEMPLATE = """
[Insert User Prompt Template Here]
User Context:
Name: {user_name}
Time: {current_date_time_str}
Content to process:
{content}
"""
# HTML Template for rendering the result in the chat
HTML_TEMPLATE = """
[Plugin Title]
[Result Title]
{result_content}
"""
class Action:
class Valves(BaseModel):
show_status: bool = Field(
default=True,
description="Whether to show operation status updates in the chat interface.",
)
LLM_MODEL_ID: str = Field(
default="",
description="Built-in LLM Model ID used for processing. If empty, uses the current conversation's model.",
)
MIN_TEXT_LENGTH: int = Field(
default=50,
description="Minimum text length required for processing (characters).",
)
# Add other configuration fields as needed
# MAX_TEXT_LENGTH: int = Field(default=2000, description="...")
def __init__(self):
self.valves = self.Valves()
def _get_user_context(self, __user__: Optional[Dict[str, Any]]) -> Dict[str, str]:
"""Extracts user context information."""
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", "User"),
"user_language": user_data.get("language", "en-US"),
}
def _get_current_time_context(self) -> Dict[str, str]:
"""Gets current time context."""
try:
# Default to a specific timezone or system time
tz = pytz.timezone("Asia/Shanghai") # Change as needed
now = datetime.now(tz)
except Exception:
now = datetime.now()
return {
"current_date_time_str": now.strftime("%Y-%m-%d %H:%M:%S"),
"current_weekday": now.strftime("%A"),
"current_year": now.strftime("%Y"),
"current_timezone_str": str(now.tzinfo) if now.tzinfo else "Unknown",
}
def _process_llm_output(self, llm_output: str) -> Any:
"""
Process the raw output from the LLM.
Override this method to parse JSON, extract Markdown, etc.
"""
# Example: Extract JSON
# try:
# start = llm_output.find('{')
# end = llm_output.rfind('}') + 1
# if start != -1 and end != -1:
# return json.loads(llm_output[start:end])
# except Exception:
# pass
return llm_output.strip()
async def _emit_status(
self,
emitter: Optional[Callable[[Any], Awaitable[None]]],
description: str,
done: bool = False,
):
"""Emits a status update event."""
if self.valves.show_status and emitter:
await emitter(
{"type": "status", "data": {"description": description, "done": done}}
)
async def _emit_notification(
self,
emitter: Optional[Callable[[Any], Awaitable[None]]],
content: str,
type: str = "info",
):
"""Emits a notification event (info, success, warning, error)."""
if emitter:
await emitter(
{"type": "notification", "data": {"type": type, "content": content}}
)
async def _emit_message(
self, emitter: Optional[Callable[[Any], Awaitable[None]]], content: str
):
"""Emits a message event (appends to current message)."""
if emitter:
await emitter({"type": "message", "data": {"content": content}})
async def _emit_replace(
self, emitter: Optional[Callable[[Any], Awaitable[None]]], content: str
):
"""Emits a replace event (replaces current message)."""
if emitter:
await emitter({"type": "replace", "data": {"content": content}})
async def action(
self,
body: dict,
__user__: Optional[Dict[str, Any]] = None,
__event_emitter__: Optional[Callable[[Any], Awaitable[None]]] = None,
__event_call__: Optional[Callable[[Any], Awaitable[Any]]] = None,
__request__: Optional[Request] = None,
) -> Optional[dict]:
logger.info(f"Action: {__name__} started")
# 1. Context Setup
user_context = self._get_user_context(__user__)
time_context = self._get_current_time_context()
# 2. Input Validation
messages = body.get("messages", [])
if not messages or not messages[-1].get("content"):
return body # Or handle error
original_content = messages[-1]["content"]
if len(original_content) < self.valves.MIN_TEXT_LENGTH:
warning_msg = f"Text too short ({len(original_content)} chars). Minimum required: {self.valves.MIN_TEXT_LENGTH}."
await self._emit_notification(__event_emitter__, warning_msg, "warning")
return body # Or return a message indicating failure
# 3. Status Notification (Start)
await self._emit_status(__event_emitter__, "Processing...", done=False)
try:
# 4. Prepare Prompt
formatted_prompt = USER_PROMPT_TEMPLATE.format(
user_name=user_context["user_name"],
current_date_time_str=time_context["current_date_time_str"],
content=original_content,
# Add other context variables
)
# 5. Determine Model
target_model = self.valves.LLM_MODEL_ID
if not target_model:
target_model = body.get("model")
# Note: No hardcoded fallback here, relies on system/user context
# 6. Call LLM
user_obj = Users.get_user_by_id(user_context["user_id"])
payload = {
"model": target_model,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": formatted_prompt},
],
"stream": False,
# "temperature": 0.5,
}
llm_response = await generate_chat_completion(
__request__, payload, user_obj
)
if not llm_response or "choices" not in llm_response:
raise ValueError("Invalid LLM response")
assistant_content = llm_response["choices"][0]["message"]["content"]
# 7. Process Output
processed_data = self._process_llm_output(assistant_content)
# 8. Generate HTML/Result
# Example: simple string replacement
final_html = HTML_TEMPLATE.replace("{result_content}", str(processed_data))
final_html = final_html.replace(
"{user_language}", user_context["user_language"]
)
# 9. Inject Result
html_embed_tag = f"```html\n{final_html}\n```"
body["messages"][-1]["content"] += f"\n\n{html_embed_tag}"
# 10. Status Notification (Success)
await self._emit_status(
__event_emitter__, "Completed successfully!", done=True
)
await self._emit_notification(
__event_emitter__, "Action completed successfully.", "success"
)
except Exception as e:
logger.error(f"Action failed: {e}", exc_info=True)
error_msg = f"Error: {str(e)}"
# Append error to chat (optional)
body["messages"][-1]["content"] += f"\n\n❌ **Error**: {error_msg}"
return body