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.

This workflow automatically processes CSV files exported from Screaming Frog to generate an `llms.txt` file that meets AI training standards. It supports multilingual environments and features intelligent URL filtering and optional AI text classification, ensuring that the extracted content is of high quality and highly relevant. Users simply need to upload the file to obtain structured data, facilitating AI model training and website content optimization, significantly enhancing work efficiency and the accuracy of data processing. The final file can be easily downloaded or directly saved to cloud storage.

This workflow builds an intelligent movie recommendation chatbot that utilizes Retrieval-Augmented Generation (RAG) technology, combining the Qdrant vector database and OpenAI language model to provide personalized movie recommendations for users. By importing rich IMDb data, it generates text vectors and conducts efficient similarity searches, allowing for a deep understanding of users' movie preferences, optimizing recommendation results, and enhancing user interaction experience. It is particularly suitable for online film platforms and movie review communities.

This workflow utilizes an automated intelligent agent to help users efficiently conduct competitor research. Users only need to input the target company's official website link, and the system can automatically identify similar companies, collect and analyze their basic information, products and services, and customer reviews. Ultimately, all data will be consolidated into a detailed report, stored in Notion, significantly enhancing research efficiency and addressing the issues of scattered information and cumbersome organization found in traditional research methods, thereby supporting market analysis and strategic decision-making.

This workflow automates the extraction of article and page content from WordPress websites to create an intelligent question-and-answer system based on retrieval-augmented generative artificial intelligence. It filters, transforms, and vectorizes the content, storing the data in a Supabase database to support efficient semantic retrieval and dynamic questioning. By integrating OpenAI's GPT-4 model, users can enjoy a more precise query experience while achieving persistent management of chat memory, enhancing the contextual continuity of interactions and increasing the intelligent utilization value of the website's content.

This workflow builds an intelligent chatbot integrated into the Slack platform, utilizing RAG technology to connect in real-time with the company's internal knowledge base. It helps employees quickly query company documents, policies, and processes. The chatbot supports natural language interaction, accurately extracting relevant information and responding in a friendly format to ensure the information is accurate and reliable. This system not only enhances the efficiency of information retrieval but also automates responses to IT support and human resources-related inquiries, significantly improving employees' work experience and communication efficiency.

This workflow can automatically extract transcribed text from specified YouTube videos, generate concise summaries, and intelligently provide question-and-answer examples related to the video content. By integrating advanced text processing and natural language generation technologies, it significantly enhances the efficiency of information retrieval, making it suitable for professionals such as content creators, educators, and market analysts, helping them quickly grasp the main points of the videos and manage knowledge for content reuse.

This workflow automatically retrieves legislative procedure data from the European Parliament's official website for the past 18 days, using advanced AI technology to intelligently filter topics related to environmental sustainability. The filtered results will be stored in Google Sheets, and Google task reminders will be generated for each relevant topic to help users efficiently track and manage legislative developments. This process significantly enhances information processing efficiency, ensuring that users can stay updated on key sustainable development policies in a timely manner.

This workflow automatically generates prompts that meet AI model requirements by receiving user queries, and it calls relevant APIs for in-depth content retrieval, extracting and outputting concise, structured answers. It can provide authoritative materials with citations, ensuring the professionalism and credibility of the results. This helps users quickly access the latest research materials in a specific field, enhancing information retrieval efficiency and content quality. It is applicable in various scenarios such as academic research, content creation, and industry analysis.

This workflow combines advanced AI language models with the Notion knowledge base to provide intelligent Q&A services. Users can input questions, and the system will automatically retrieve relevant content and generate accurate answers, along with links to Notion pages, ensuring the reliability and traceability of the information. This assistant enhances the efficiency of knowledge queries and is suitable for various scenarios such as internal knowledge management in enterprises, customer support, and personal information retrieval, helping users quickly access the information they need.

This workflow provides an intelligent personal shopping assistant feature for e-commerce platforms by integrating language models and retrieval-augmented generation technology. It can automatically identify users' shopping needs, accurately extract product search information, and match relevant products in the WooCommerce database. Additionally, for non-shopping inquiries, the system offers intelligent responses based on a knowledge base, enhancing the user experience. Through context management, it ensures the continuity of conversations, significantly improving customer satisfaction and service efficiency.

This workflow automatically scrapes the latest articles from the Paul Graham website, extracts and processes the text content, and converts it into vectors stored in the Milvus database. By integrating OpenAI's embedding model, it enables intelligent Q&A and information retrieval based on the knowledge base. It supports manual triggers and chat message triggers for AI responses, making it suitable for researchers, businesses, and content creators, enhancing information management and retrieval efficiency, and streamlining the knowledge base construction process.

This workflow builds an intelligent question-and-answer system based on company documents stored in Google Drive, utilizing a vector database and large language models to achieve rapid information retrieval and natural language interaction. By automatically synchronizing document updates, employees can obtain concise and accurate answers related to policies and processes in real time, thereby enhancing knowledge management efficiency, optimizing the self-service experience, and addressing the issues of traditional document fragmentation and retrieval difficulties. It is applicable to various scenarios, including internal knowledge bases, HR policy inquiries, and intelligent retrieval of legal compliance documents.

This workflow intelligently captures posts tagged with #damus on the Nostr social platform, utilizes AI models to analyze discussion topics, and automatically generates detailed topic reports. It distributes these reports through multiple channels, including Gmail and Telegram. This effectively addresses the cumbersome process of manually filtering information, helping community operation teams, product managers, and content creators quickly obtain valuable insights, enhance information retrieval efficiency, and achieve intelligent management and dissemination of data.

This workflow utilizes the Google Gemini 1.5 AI model to automatically analyze YouTube videos, generating diverse content such as summaries, verbatim transcriptions, timestamps, and scene descriptions. Users can dynamically adjust the prompts based on their needs to achieve precise information extraction. The processing results can be saved to Google Drive and sent via email for easy access and sharing. This tool significantly enhances the efficiency of obtaining video content, making it suitable for content creators, marketers, educational institutions, and general viewers, saving time and improving information utilization.

This workflow combines real-time web scraping with AI chatbot technology, enabling it to automatically retrieve the latest web content based on user queries and generate accurate responses. Users can obtain precise information quickly by asking questions in natural language, without the need for manual searches, significantly enhancing the efficiency of information retrieval and the interaction experience. It is suitable for users who require real-time information, such as corporate customer service representatives, market analysts, and researchers, helping them make decisions and respond more efficiently.

This workflow automatically scrapes popular posts from specified Reddit communities, utilizing AI for content analysis and sentiment assessment to help users identify business-related opportunities and pain points. It can generate innovative business proposals tailored to specific issues and structurally store the analysis results in Google Sheets for easier management and tracking. Additionally, the classification and saving function for email drafts effectively supports follow-up, enabling entrepreneurs and market research teams to quickly gain insights into market dynamics and enhance decision-making efficiency.

This workflow integrates AI technology and automation tools to achieve intelligent monitoring and summary pushing of thematic information. It regularly retrieves the latest articles from multiple RSS sources, uses AI for relevance classification and content extraction, generates structured summaries in Slack format, and promptly pushes them to designated channels. This enables users to efficiently stay updated on the latest developments in their areas of interest, addressing issues of information overload and inconvenient sharing, thereby enhancing team collaboration and information processing efficiency.

This workflow is designed to automate the testing and analysis of the performance of multiple large language models locally. By dynamically retrieving the list of models and standardizing system prompts, users can easily compare the output performance of different models on specific tasks. The workflow records request and response times, conducts multi-dimensional text analysis, and structures the results for storage in Google Sheets, facilitating subsequent management and comparison. Additionally, it supports flexible parameter configuration to meet diverse testing needs, enhancing the efficiency and scientific rigor of model evaluation.

This workflow receives PDF files via Telegram, automatically splits them, and converts the content into vectors stored in the Pinecone database, supporting vector-based intelligent Q&A. Users can conveniently query document information in the chat window, significantly improving the speed and accuracy of knowledge acquisition. It is suitable for scenarios such as enterprise document management, customer support, and education and training, greatly enhancing information retrieval efficiency and user experience.

This workflow is an intelligent orchestration system that efficiently processes and optimizes content using a multi-agent architecture. It dynamically schedules various AI agents, such as content summarization, review, and guidance instructions, in conjunction with human oversight to ensure high-quality output. The system supports content version management and automatic synchronization to GitHub, creating a closed-loop knowledge management process that is suitable for complex document generation and review, enhancing the efficiency of content production and quality assurance in enterprises. This process achieves a perfect combination of intelligence and human supervision.

This workflow utilizes a powerful language model to automatically process natural language requests and generate structured and standardized output data. Its key highlight is the integration of an automatic output correction parser, which can intelligently correct outputs that do not meet expectations, thereby ensuring the accuracy and consistency of the data. Additionally, the workflow defines a strict JSON Schema for output validation, addressing the issue of lack of structure in traditional language model outputs. This significantly reduces the costs associated with manual verification and correction, making it suitable for various automated tasks that require high-quality data.

The Intelligent Text Fact-Checking Assistant efficiently splits the input text sentence by sentence and conducts fact-checking, using a customized AI model to quickly identify and correct erroneous information. This tool generates structured reports that list incorrect statements and provide an overall accuracy assessment, helping content creators, editorial teams, and research institutions enhance the accuracy and quality control of their texts. It addresses the time-consuming and labor-intensive issues of traditional manual review and is applicable in various fields such as news, academia, and content moderation.

This workflow integrates a vector database and a multilingual embedding model to achieve intelligent document processing and a question-answering system. It can automatically monitor and process PDF files in Google Drive, extract text, and generate vectors, supporting efficient semantic retrieval and intelligent responses. Users can quickly access a vast amount of document information, enhancing the management and query efficiency of multilingual content. It is suitable for scenarios such as enterprise knowledge bases, customer service robots, and automatic indexing and querying in specialized fields.