""" title: Deep Reading & Summary author: Fu-Jie author_url: https://github.com/Fu-Jie funding_url: https://github.com/Fu-Jie/awesome-openwebui version: 0.1.0 icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwYXRoIGQ9Ik0yIDNIMGEyIDIgMCAwIDAgMiAyIi8+PHBhdGggZD0iTTIyIDNIMjBhMiAyIDAgMCAwLTIgMiIvPjxwYXRoIGQ9Ik0yIDdoMjB2MTRhMiAyIDAgMCAxLTIgMmgtMTZhMiAyIDAgMCAxLTItMnYtMTQiLz48cGF0aCBkPSJNMTEgMTJ2NiIvPjxwYXRoIGQ9Ik0xNiAxMnY2Ii8+PHBhdGggZD0iTTYgMTJ2NiIvPjwvc3ZnPg== description: Provides deep reading analysis and summarization for long texts. requirements: jinja2, markdown """ from pydantic import BaseModel, Field from typing import Optional, Dict, Any import logging import re from fastapi import Request from datetime import datetime import pytz import markdown from jinja2 import Template from open_webui.utils.chat import generate_chat_completion from open_webui.models.users import Users logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) logger = logging.getLogger(__name__) # ================================================================= # Internal LLM Prompts # ================================================================= SYSTEM_PROMPT_READING_ASSISTANT = """ 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. Please provide the following: 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. 2. **Key Information Points**: List 5-8 most important facts, viewpoints, or arguments. Each point should: - Be specific and insightful - Include necessary details and context - Use Markdown list format 3. **Actionable Advice**: Identify and refine specific, actionable items from the text. Each suggestion should: - Be clear and actionable - Include execution priority or timing suggestions - If there are no clear action items, provide learning suggestions or thinking directions Please strictly follow these guidelines: - **Language**: All output must be in the user's specified language. - **Format**: Please strictly follow the Markdown format below, ensuring each section has a clear header: ## Summary [Detailed summary content here, 2-3 paragraphs, use Markdown **bold** or *italic* to emphasize key points] ## Key Information Points - [Key Point 1: Include specific details and context] - [Key Point 2: Include specific details and context] - [Key Point 3: Include specific details and context] - [At least 5, at most 8 key points] ## Actionable Advice - [Action Item 1: Specific, actionable, include priority] - [Action Item 2: Specific, actionable, include priority] - [If no clear action items, provide learning suggestions or thinking directions] - **Depth First**: Analysis should be deep and comprehensive, not superficial. - **Action Oriented**: Focus on actionable suggestions and next steps. - **Analysis Results Only**: Do not include any extra pleasantries, explanations, or leading text. """ USER_PROMPT_GENERATE_SUMMARY = """ Please conduct a deep analysis of the following long text, providing: 1. Detailed Summary (2-3 paragraphs, comprehensive overview) 2. Key Information Points List (5-8 items, including specific details) 3. Actionable Advice (Specific, clear, including priority) --- **User Context:** User Name: {user_name} Current Date/Time: {current_date_time_str} Weekday: {current_weekday} Timezone: {current_timezone_str} User Language: {user_language} --- **Long Text Content:** ``` {long_text_content} ``` Please conduct a deep and comprehensive analysis, focusing on actionable advice. """ # ================================================================= # Frontend HTML Template (Jinja2 Syntax) # ================================================================= HTML_TEMPLATE = """ Deep Reading: Deep Analysis Report

šŸ“– Deep Reading: Deep Analysis Report

User: {{ user_name }} Analysis Time: {{ current_date_time_str }} Weekday: {{ current_weekday }}

šŸ“Detailed Summary

{{ summary_html | safe }}

šŸ’”Key Information Points

{{ keypoints_html | safe }}

šŸŽÆActionable Advice

{{ actions_html | safe }}

© {{ current_year }} Deep Reading - Deep Text Analysis Service

""" 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 text analysis. If empty, uses the current conversation's model.", ) MIN_TEXT_LENGTH: int = Field( default=200, description="Minimum text length required for deep analysis (characters). Recommended 200+.", ) RECOMMENDED_MIN_LENGTH: int = Field( default=500, description="Recommended minimum text length for best analysis results.", ) def __init__(self): self.valves = self.Valves() def _process_llm_output(self, llm_output: str) -> Dict[str, str]: """ Parse LLM Markdown output and convert to HTML fragments. """ summary_match = re.search( r"##\s*Summary\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL | re.IGNORECASE ) keypoints_match = re.search( r"##\s*Key Information Points\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL | re.IGNORECASE, ) actions_match = re.search( r"##\s*Actionable Advice\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL | re.IGNORECASE, ) summary_md = summary_match.group(1).strip() if summary_match else "" keypoints_md = keypoints_match.group(1).strip() if keypoints_match else "" actions_md = actions_match.group(1).strip() if actions_match else "" if not any([summary_md, keypoints_md, actions_md]): summary_md = llm_output.strip() logger.warning( "LLM output did not follow expected Markdown format. Treating entire output as summary." ) # Use 'nl2br' extension to convert newlines \n to
md_extensions = ["nl2br"] summary_html = ( markdown.markdown(summary_md, extensions=md_extensions) if summary_md else '

Failed to extract summary.

' ) keypoints_html = ( markdown.markdown(keypoints_md, extensions=md_extensions) if keypoints_md else '

Failed to extract key information points.

' ) actions_html = ( markdown.markdown(actions_md, extensions=md_extensions) if actions_md else '

No explicit actionable advice.

' ) return { "summary_html": summary_html, "keypoints_html": keypoints_html, "actions_html": actions_html, } def _build_html(self, context: dict) -> str: """ Build final HTML content using Jinja2 template and context data. """ template = Template(HTML_TEMPLATE) return template.render(context) 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("%Y-%m-%d %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\nšŸ’” Tip: For short texts, consider using 'āš” 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"āš ļø {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": "šŸ“– Deep Reading started, analyzing deeply...", }, } ) if self.valves.show_status: await __event_emitter__( { "type": "status", "data": { "description": "šŸ“– 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, } final_html_content = self._build_html(context) html_embed_tag = f"```html\n{final_html_content}\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": "šŸ“– Deep Reading: Analysis complete!", "done": True, }, } ) await __event_emitter__( { "type": "notification", "data": { "type": "success", "content": f"šŸ“– 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\nāŒ **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