WhatsApp Intelligent Sales Assistant

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.

This workflow utilizes Perplexity AI's Sonar Pro model to provide intelligent Q&A functionality. Users can customize system prompts and questions, as well as flexibly set query domains. Through API integration, it automatically extracts and cleans the returned answers, enhancing the efficiency and accuracy of information retrieval. It is suitable for various scenarios such as customer service responses, market research, and internal training, helping users quickly obtain structured authoritative answers and reducing the cumbersome steps of manual searching.

This workflow is designed to automate the generation of research reports based on user-defined topics, integrating various information sources such as OpenAI, Wikipedia, news APIs, Google Search, and Google Scholar. Through intelligent analysis and integration, it produces structured PDF reports that include an introduction to the topic, key findings, and academic insights, which are automatically sent to designated users via Gmail and Telegram. Additionally, all data is recorded in Google Sheets for easy management and tracking, significantly enhancing research efficiency and the accuracy of information integration.

This workflow utilizes RAG technology, combined with the Pinecone vector database and OpenAI intelligent models, to build an intelligent Q&A chatbot for the GitHub API. It can real-time scrape and index GitHub's API documentation, quickly answering users' technical queries through vector search and semantic understanding, significantly improving the efficiency and accuracy of developers in obtaining interface information. It is suitable for scenarios such as technical support, documentation maintenance, and training.

This workflow builds an intelligent chatbot that combines the GPT-4o language model with MCP Brave Search, enabling it to process user chat messages in real-time and perform web searches. The chatbot not only generates high-quality intelligent responses but also supports short-term memory, enhancing the coherence of conversations and the user experience. It is suitable for various scenarios such as automated customer service, knowledge retrieval, and information inquiry, helping users quickly obtain the information they need and improving interaction efficiency.

This workflow creates a multilingual No-Code tool query bot. When users input the tool name in Telegram, the bot automatically retrieves detailed information from a remote database and translates it into the user's native language, subsequently sending it as a multimedia message. Through this process, users can easily access introductions to No-Code tools, overcoming language barriers and achieving instant information retrieval. This greatly enhances the convenience and user-friendliness of inquiries, making it suitable for technical support and educational training in multilingual environments.