Business WhatsApp AI RAG Chatbot

This workflow utilizes AI intelligent agents and custom tools to enable quick querying and intelligent responses for popular post data on the Hacker News platform. Users can obtain the top 50 most popular posts and detailed information through natural language commands. It includes built-in data cleaning and formatting features to ensure the results are clearly structured. This is suitable for technical researchers, content creators, and developers, helping them to rapidly access authoritative and up-to-date technology information, thereby enhancing the efficiency of information retrieval.

This workflow integrates with the Bitrix24 open channel to implement an intelligent chatbot application that features efficient question-and-answer capabilities based on Retrieval-Augmented Generation (RAG) technology. It can automatically register the bot, handle user messages, and provide intelligent responses based on the content of uploaded documents. The documents are stored and retrieved using a vector database, combined with advanced chat models, which enhances the accuracy of answers and contextual understanding, making it suitable for scenarios such as internal knowledge management and customer support within enterprises.

This workflow combines intelligent chat models, vector retrieval technology, and e-commerce platforms to provide users with personalized shopping assistant services. It can automatically identify users' shopping needs, accurately extract product search information, and query inventory in real-time to recommend suitable products. Additionally, for inquiries about store information, the system can also provide intelligent responses, supporting context management for multi-turn conversations, thereby enhancing the user shopping experience and satisfaction.

This workflow utilizes artificial intelligence technology to achieve automated information monitoring and summary generation. It regularly fetches articles from designated RSS sources, classifies content relevance using an AI model, generates summaries suitable for Slack format, and pushes them to specified channels. Additionally, it uses Google Sheets to manage the source list and processed articles, preventing duplicate monitoring, enhancing information processing efficiency, and helping the team quickly access industry trends and key information.

This workflow automates the extraction and intelligent summarization of the latest articles from Paul Graham's official website. Users only need to trigger it with a single click, and the system will extract the article links, retrieve the main content, and generate a summary using the GPT-4o-mini model. The final output includes the article title, summary, and link. This process is efficient and time-saving, making it ideal for content creators, researchers, and anyone interested in Paul Graham's ideas, helping them quickly access and understand the essence of the articles and improve information processing efficiency.

This workflow automatically crawls the latest academic paper information from the Hugging Face website at regular intervals, using the OpenAI GPT-4 model for in-depth analysis and information extraction. The structured results are ultimately stored in a Notion database. By employing scheduled triggers, duplicate data filtering, and batch processing, it significantly enhances the literature collection efficiency for academic researchers and data organizers, ensuring that the information is well-organized and easy to retrieve, thus addressing the cumbersome issues of manual searching and organizing.

This workflow integrates a voice and chatbot building platform, calendar management, and retrieval-augmented generation technology, providing intelligent customer service and voice assistant functionalities. It supports customer order status inquiries, appointment management, and knowledge-based product consultations, enhancing customer experience and service efficiency. By automating scheduling and real-time issue response, it helps businesses achieve multi-channel customer service, suitable for scenarios such as electronic product retail, online customer support, and technical assistance, significantly improving service quality and customer satisfaction.

This workflow builds an intelligent voice chatbot that combines voice interaction and natural language processing technologies. It can quickly retrieve information from a document knowledge base and respond to user inquiries in voice format. By implementing efficient semantic retrieval through a vector database, along with intelligent question-answer generation and multi-turn dialogue memory, it enhances the user experience. It is suitable for scenarios such as enterprise customer service, smart navigation, and education and training, lowering the barriers to building voice assistants and facilitating rapid responses to customer needs.