Make OpenAI Citation for File Retrieval RAG

This workflow is designed to automate the scraping of the latest articles from Paul Graham's official website, extracting article links and obtaining titles and body content. It utilizes the OpenAI GPT-4 model to intelligently generate article summaries, ultimately integrating structured data that includes titles, summaries, and links. Through this process, users can efficiently acquire and understand Paul Graham's core insights, making it applicable to various scenarios such as content planning, research, and media editing, significantly enhancing information processing efficiency.

This workflow automatically receives YouTube video links through an interface, extracts video information and subtitles, and utilizes a large language model to perform structured summarization and analysis of the subtitles, generating clear technical summaries. At the same time, it provides real-time feedback of the analysis results to the caller and pushes the video title and link via Telegram, significantly enhancing the efficiency of video content processing and helping users quickly understand the core information of the video. It is applicable in various fields such as education, content creation, research, and enterprise knowledge management.

This workflow implements automatic monitoring and processing of PDF files in a specific folder on Google Drive, including file downloading, content extraction, and cleaning. The processed document content is converted into vector embeddings and stored in a Pinecone database, while also supporting users in intelligent Q&A through a chat interface, providing accurate answers by incorporating contextual information. This process enhances document management efficiency and simplifies information retrieval, making it suitable for businesses and teams to quickly access the required document information.

This workflow automatically monitors the status of Epic tasks in Jira. Once marked as "Done," it retrieves the relevant issues and comments, and uses AI analysis to generate a detailed agile retrospective report. Finally, the report is automatically updated in a structured Markdown format to a designated Google Docs document, ensuring that the content is clear and standardized, making it easy for the team to share and archive. This significantly improves the team's efficiency and quality in project summarization and experience sharing.

This workflow utilizes intelligent methods to automatically analyze quarterly financial reports of publicly listed companies, extract key information, and generate structured financial analysis reports. It combines vector databases and AI technology to quickly identify financial trends and anomalies, improving analysis efficiency and reducing human errors. The final report is automatically saved to Google Docs for easy viewing and sharing, making it suitable for financial analysts, investors, and corporate finance teams, thereby supporting informed decision-making and in-depth insights.

This workflow seamlessly integrates an AI smart assistant with Google Drive, providing intelligent Q&A services based on document content. Users can upload important documents, and the system automatically updates the assistant's knowledge base, enabling it to respond to customer inquiries in a professional and friendly manner. This automated process significantly enhances the speed and accuracy of customer consultations, making it particularly suitable for travel agencies and business scenarios that require document support, helping companies reduce labor costs and improve service quality.

This workflow can automatically receive research topics submitted by users and utilize Perplexity AI for in-depth information retrieval and content generation. Through a multi-step AI model processing, the workflow structures the research results and converts them into modern, responsive HTML webpages, beautified with Tailwind CSS. This process achieves full automation from topic research to webpage presentation, making it particularly suitable for content creators and researchers to quickly generate professional webpages, enhance work efficiency, and simplify the information integration and design process.

This intelligent dialogue agent workflow combines advanced language models with information retrieval tools, featuring contextual memory capabilities that allow it to respond to user chat messages in real time. By retaining recent conversation records and accessing external data sources, the workflow effectively addresses the issues of inaccurate responses and outdated information commonly found in traditional chatbots. It is suitable for various scenarios such as customer service, intelligent Q&A systems, and educational tutoring, enhancing the coherence and richness of conversations while providing users with a high-quality intelligent interaction experience.