Google Doc Summarizer to Google Sheets

This workflow can automatically monitor a specified Google Drive folder, real-time retrieve the content of newly uploaded Google Docs, and generate intelligent summaries using an AI model. The summaries and the information of the document uploaders will be automatically saved to Google Sheets, facilitating later management and quick reference. This process significantly improves document management efficiency, reduces the time spent on manual organization, and minimizes the risk of omissions, making it suitable for businesses, teams, and educational institutions that need to quickly obtain and organize document information.

Tags

Smart SummaryGoogle Sheets

Workflow Name

Google Doc Summarizer to Google Sheets

Key Features and Highlights

This workflow automatically monitors newly uploaded Google Docs within a specified Google Drive folder, retrieves document content in real-time, and performs intelligent summarization using an AI model based on GPT-4o-mini. The summary results, along with metadata such as the uploader’s name and email, are automatically appended and saved into Google Sheets for unified management and quick reference.

Core Problems Addressed

Manual compilation and summarization of Google Docs content is time-consuming and prone to missing critical information. This workflow effectively eliminates the hassle of manual processing by automating content extraction, intelligent summarization, and systematic storage, significantly enhancing document management efficiency and the value of information utilization.

Application Scenarios

  • Enterprises or teams needing to regularly consolidate large volumes of Google Docs content to quickly obtain key information
  • Project management for tracking document updates and automatically generating summary reports
  • Educational institutions or research teams organizing materials and rapidly producing literature summaries
  • Content creators archiving document summaries for easier subsequent retrieval and analysis

Main Process Steps

  1. Monitor newly created document files in a specified Google Drive folder via a Google Drive trigger
  2. Retrieve and read the content of the latest uploaded Google Docs
  3. Invoke the OpenAI GPT-4o-mini model to generate intelligent summaries of the document content
  4. Append the summary and related document metadata (uploader’s name, email, etc.) into a Google Sheets spreadsheet
  5. Achieve automatic consolidation and structured storage of document content

Involved Systems or Services

  • Google Drive (file monitoring and content retrieval)
  • Google Docs (document content reading)
  • OpenAI GPT-4o-mini model (AI-powered intelligent summarization)
  • Google Sheets (storage of summaries and metadata)

Target Users and Value

Ideal for business managers, project leaders, content operators, educators, researchers, and anyone requiring efficient management and rapid access to key information from large volumes of Google Docs. By automating the workflow, it significantly reduces time spent on organization, improves information utilization efficiency, and supports decision-making and collaborative work.

Recommend Templates

Travel AssistantAgent

This workflow builds an intelligent travel assistant that integrates large language models and vector search technology to achieve personalized travel recommendations and intelligent Q&A functions. Through dynamic data reception and chat memory, users can receive real-time updates on travel information, enhancing the interactive experience. At the same time, the system addresses issues such as the isolation of traditional travel information, inaccurate recommendations, and incoherent interactions, making it suitable for online travel platforms, travel agencies, and personal travel planning, significantly improving service intelligence and travel efficiency.

Smart TravelVector Search

Open Deep Research - AI-Powered Autonomous Research Workflow

This workflow utilizes advanced artificial intelligence technology to automate the execution of in-depth research tasks. Users only need to input the research topic, and the system can generate precise search queries, conduct multiple rounds of online searches, and integrate information from various authoritative sources through intelligent analysis. Ultimately, the workflow produces a structured research report in Markdown format, significantly enhancing research efficiency and information accuracy. It is suitable for various scenarios such as academic research, market analysis, and product research, helping users quickly obtain comprehensive and valuable research results.

AI ResearchAutomation Survey

Hugging Face to Notion

This workflow automates the retrieval of the latest academic papers from Hugging Face, utilizing the advanced GPT-4 model for in-depth analysis and structured extraction of paper abstracts. Ultimately, it intelligently stores key information in a Notion database. It effectively addresses the tediousness of manually searching for papers, avoids redundant information storage, and provides efficient management of academic resources. This is suitable for researchers, academic institutions, and AI practitioners to continuously track the latest research developments, enhancing the efficiency and quality of literature organization.

Academic PaperSmart Analysis

DSP Agent

The DSP Agent is an intelligent learning assistant specifically designed for students in the field of signal processing. It receives text and voice messages through Telegram and utilizes advanced AI models to provide instant knowledge queries, calculation assistance, and personalized learning tracking. This tool helps students quickly understand complex concepts, offers dynamic problem analysis and learning suggestions, addressing the issues of insufficient interactivity and lack of personalized tutoring in traditional learning. It enhances learning efficiency and experience.

Smart LearningSignal Processing

RAG on Living Data

This workflow implements a Retrieval-Augmented Generation (RAG) function through real-time data updates, automatically retrieving the latest content from the Notion knowledge base. It performs text chunking and vectorization, storing the results in the Supabase vector database. By integrating OpenAI's GPT-4 model, it provides contextually relevant intelligent Q&A, significantly enhancing the efficiency and accuracy of knowledge base utilization. This is applicable in scenarios such as enterprise knowledge management, customer support, and education and training, ensuring that users receive the most up-to-date information.

Intelligent QAVector Search

A/B Split Testing

This workflow implements a session-based A/B split testing, which can randomly assign different prompts (baseline and alternative) to users in order to evaluate the effectiveness of language model responses. By integrating a database to record sessions and allocation paths, and combining it with the GPT-4o-mini model, it ensures continuous management of conversation memory, enhancing the scientific rigor and accuracy of the tests. It is suitable for AI product development, chatbot optimization, and multi-version effectiveness verification, helping users quickly validate prompt strategies and optimize interaction experiences.

A/B TestingPrompt Optimization

Get Airtable Data in Obsidian Notes

This workflow enables real-time synchronization of data from the Airtable database to Obsidian notes. Users simply need to select the relevant text in Obsidian and send a request. An intelligent AI agent will understand the query intent and invoke the OpenAI model to retrieve the required data. Ultimately, the results will be automatically inserted into the notes, streamlining the process of data retrieval and knowledge management, thereby enhancing work efficiency and user experience. It is suitable for professionals and team collaboration users who need to quickly access structured data.

Obsidian IntegrationAirtable Sync

CoinMarketCap_AI_Data_Analyst_Agent

This workflow builds a multi-agent AI analysis system that integrates real-time data from CoinMarketCap, providing comprehensive insights into the cryptocurrency market. Users can quickly obtain analysis results for cryptocurrency prices, exchange holdings, and decentralized trading data through Telegram. The system can handle complex queries and automatically generate reports on market sentiment and trading data, assisting investors and researchers in making precise decisions, thereby enhancing information retrieval efficiency and streamlining operational processes.

Crypto AnalysisMulti-Agent