piepdrive-test

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.

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.

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.

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.

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.

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.

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.

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.