This workflow can automatically scrape the latest news articles from specified news websites without relying on RSS subscriptions. It regularly extracts article links, publication dates, titles, and body content, and uses the GPT-4 model to generate brief summaries and extract key technical keywords. The organized structured data will be stored in a NocoDB database, facilitating subsequent retrieval and analysis, significantly improving the efficiency of news monitoring and content management, making it suitable for use by businesses, media, and data analysts.

This workflow utilizes AI language models and various data sources to achieve automated deep information retrieval and research report generation. After the user inputs a query, the system generates precise search keywords, conducts web searches using SerpAPI, and combines content analysis with Jina AI, ultimately integrating the results into a structured research report. This process enhances research efficiency, ensures the coherence and accuracy of information extraction, and is applicable in scenarios such as academic research, market research, content creation, and corporate decision-making, helping users quickly obtain high-quality materials.

This workflow integrates an intelligent assistant and vector storage, aiming to achieve smart Q&A after document retrieval and automatically add literature citations to the retrieved content. Users can format the output results as Markdown or HTML, facilitating the generation of professional documents with dynamic citation numbers, thereby enhancing the credibility and traceability of the information. It is suitable for fields such as research, education, and law, addressing issues of missing citations and strange characters in answers, and helping users efficiently generate standardized documents.

This workflow is capable of automatically loading text prompt files from a specified GitHub repository, extracting and replacing variable placeholders, and generating complete prompt content for use by AI models. It features a variable validation mechanism to ensure that all required variables are correctly assigned, preventing errors and improving efficiency. Additionally, by integrating the Ollama chat model and LangChain AI Agent, it achieves full-process automation from text prompts to intelligent responses, making it suitable for various scenarios that require dynamic content generation.

This workflow automatically fetches the latest artificial intelligence news from mainstream news APIs at a scheduled time every day. It then filters, summarizes, and translates the news into Traditional Chinese using advanced AI models. Finally, the organized news summaries are promptly pushed to designated Telegram chat groups or channels, helping users efficiently access cutting-edge AI information. This solution addresses the cumbersome issues of manual searching and translation, ensuring the timeliness and continuity of information, making it suitable for various AI industry professionals and general users.

This workflow automatically extracts the transcription text from a YouTube video by inputting the video ID and performs intelligent summarization. After obtaining the text using the SearchApi, it undergoes multiple steps of splitting and content merging, combined with the OpenAI GPT-4 model to generate a concise summary. This process effectively addresses the challenge of quickly extracting key information from long videos, making it suitable for content creators, educators, and market researchers, significantly improving the efficiency and accuracy of information retrieval.

This workflow can automatically identify and extract license plate numbers from uploaded vehicle images, directly returning clean license plate characters, eliminating the need for manual input by users. By integrating advanced large language models, it significantly improves the efficiency and accuracy of license plate recognition, streamlining the traditional license plate extraction process. It is applicable in various scenarios such as traffic management, parking lots, and logistics monitoring, helping users achieve rapid automated collection of vehicle information, enhance management intelligence, and save time and labor costs.

The Tech Radar workflow automates the management and intelligent querying of enterprise technology radar data by integrating various technologies. It transforms data from Google Sheets into structured text and stores it in vector and relational databases, supporting multidimensional queries. Equipped with an intelligent AI agent, it can accurately respond to user inquiries, enhancing information retrieval efficiency. Additionally, scheduled synchronization updates ensure data timeliness, lowering the information access barrier for non-technical personnel and facilitating technology decision-making and internal communication.

This workflow integrates RSS feeds from multiple mainstream cryptocurrency news sources and utilizes advanced AI models for intelligent analysis. It automatically extracts keywords and filters relevant reports to generate news summaries and market sentiment analysis. Ultimately, the results are pushed to users in real-time via a Telegram bot, helping investors and analysts efficiently access personalized cryptocurrency news and market trends, thereby addressing the cumbersome issue of information filtering.

This workflow creates an automated user interview system that utilizes AI smart agents to guide the interviews and dynamically generate open-ended questions. Users respond through an online form, and the system records the conversation in real-time, allowing the interview to be ended at any time. Interview data is quickly stored in Redis and can be exported to Google Sheets for easier subsequent analysis. This system reduces the labor costs associated with traditional interviews and provides an efficient interview experience available 24/7, making it suitable for various scenarios such as market research, product feedback, and educational institutions.

This workflow is designed to achieve automatic extraction and intelligent parsing of content from PDF documents. Users can directly upload PDF files without the need for OCR recognition, simplifying the process. It simultaneously utilizes two AI models, Claude 3.5 Sonnet and Gemini 2.0 Flash, allowing for a comparison of their performance in data extraction effectiveness, response speed, and cost. It supports customizable extraction instructions, and the output can be adjusted to JSON format, making it suitable for extracting key information from documents such as financial invoices and contracts, thereby enhancing data processing efficiency and automation levels.

This workflow achieves content-based intelligent querying by automatically retrieving and processing files stored in Supabase, combined with OpenAI's text embedding technology. It effectively deduplicates, extracts PDF and text content, and stores it in a vectorized format, supporting fast and accurate information retrieval. It is suitable for scenarios such as enterprise knowledge base management, customer support, and professional document querying, significantly enhancing document management efficiency and user interaction experience.

This workflow utilizes an AI language model to automate user interviews, capable of generating open-ended questions and recording user responses in real-time. Users initiate the interview through a form, and the interview data is stored in a Redis database and synchronized to Google Sheets for easy data analysis and sharing. Users can end the interview at any time, and the interview records can be accessed via a Webhook, ensuring data security and efficient management. This system is suitable for market research, user experience studies, and academic surveys, greatly enhancing the flexibility and efficiency of interviews.

This workflow aims to create an OpenAI smart assistant integrated with Google Drive, capable of automatically downloading and converting documents, and dynamically updating the assistant's knowledge base using the GPT model. Through contextual memory, the assistant enables multi-turn conversations, providing coherent and accurate responses, suitable for scenarios such as travel services, corporate knowledge management, and educational resource assistance. Users can easily build a personalized intelligent Q&A system, enhancing service efficiency and user experience.

This workflow automatically generates high-quality exam questions from the content of articles in Google Docs using AI technology, including open-ended questions and multiple-choice questions. By combining vector databases with advanced language models, the process can deeply understand the document's content, extract key knowledge points, and quickly generate exam questions that meet educational needs. This significantly improves the efficiency of question creation while ensuring the quality and diversity of the questions, making it suitable for various scenarios such as educational institutions, online training platforms, and corporate training.

This workflow can automatically fetch the top news headlines from the Hacker News homepage for a specified date, utilize a large language model for intelligent categorization and trend analysis, generate themed Markdown news summaries, and push them to subscribed users via a Telegram channel. It addresses the issues of historical news data aggregation and information overload, helping users quickly grasp technological trends and hot topics. It is suitable for technology media, researchers, and information service providers, enhancing the timeliness and value of the content.

This workflow combines LangChain and the OpenAI GPT-4 model to enable intelligent question-and-answer queries of historical workflow data. Users can ask questions in natural language, and the system automatically retrieves and analyzes relevant data to provide accurate answers. This process simplifies information retrieval, enhances data utilization, and is suitable for scenarios such as enterprise knowledge base queries, customer information retrieval, and data analysis, helping users quickly obtain key information and improve decision-making efficiency.

This workflow is an AI-based legal assistant that can automatically download and parse PDF documents of tax laws from Texas, storing the structured data in a vector database. Users can ask questions through a chat interface, and the system will intelligently retrieve relevant provisions and provide accurate answers. By combining vector search and intelligent Q&A technology, this workflow simplifies the process of querying tax laws and enhances the efficiency of accessing legal information, making it suitable for various fields such as legal consulting, tax work, and education and training.

This workflow enhances chat response capabilities in real-time by combining the Google Gemini large language model with Bright Data's search engine tools. It can automatically retrieve the latest web search results from Google, Bing, and Yandex, generating high-quality conversational answers that improve the accuracy and relevance of responses. Additionally, it supports Webhook notifications to ensure real-time alerts for users, making it suitable for scenarios such as intelligent customer service, market research, and AI-assisted decision-making.

This workflow utilizes Jina AI's deep search API to automate efficient AI-driven research, generating detailed structured reports. Users can input queries in natural language without the need for an API key, completely free of charge. The output is in an easily readable Markdown format, including source links and footnotes for easy citation and sharing. This tool helps researchers, analysts, and content creators quickly obtain authoritative analysis results, significantly enhancing research efficiency and quality, and is suitable for various professional scenarios.

This workflow is an intelligent sales assistant that receives customer inquiries via WhatsApp and utilizes advanced AI technology and vector retrieval to provide real-time answers to users regarding Yamaha's 2024 powered speakers. It features multi-turn conversation memory and automatic response capabilities, enabling it to efficiently handle customer questions, enhance service quality and satisfaction, and assist businesses in achieving automated customer support and improved sales efficiency.

This workflow can automatically extract text from Google Drive documents, using a context-aware approach for chunk processing. It converts the text chunks into vectors through OpenRouter and Google Gemini, and stores them in the Pinecone database. Its main advantage lies in improving the accuracy and relevance of document retrieval, avoiding the shortcomings of traditional search methods in semantic understanding. It is suitable for various scenarios such as enterprise knowledge base construction, large document management, and intelligent question-and-answer systems, achieving full-process automation of document handling.

This workflow automatically scrapes publicly available content from WordPress websites and utilizes generative AI and vector databases to create an intelligent Q&A system. It converts article and page content into Markdown format and generates vector representations to support rapid semantic retrieval. Users can ask questions in real-time, and the system generates accurate answers by combining relevant content, enhancing the interactive experience of the website. This solution is suitable for businesses or personal websites that require intelligent customer service and knowledge management, ensuring that content is always up-to-date and efficiently serves visitors.

This workflow combines Confluence cloud documents with an AI chatbot. Users can ask questions through a chat interface, and the system automatically calls an API to retrieve relevant page content, utilizing the GPT-4 model for intelligent Q&A. It supports multi-turn conversation memory to ensure contextual coherence and can push results via Telegram, enhancing information retrieval efficiency. This facilitates internal knowledge management, technical document queries, and customer support, enabling fast and accurate information access.