Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI)

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.

This workflow combines AI language models with the Open-Meteo weather forecast API to provide intelligent weather inquiry and forecasting services. Users can simply enter the city name and their requirements through a chat interface, and the AI will automatically obtain the geographic coordinates and retrieve weather information, generating accurate weather forecast responses. This process significantly simplifies the traditional weather inquiry operations, enhances interaction efficiency, and is suitable for various scenarios such as smart customer service, travel planning, and education and training, meeting users' needs for real-time weather information.

This in-depth research workflow helps users efficiently conduct research on complex topics through automated searches and content scraping, combined with advanced language models. After the user inputs the research topic, the system generates multiple search queries and filters relevant information, supporting dynamic adjustments to the depth and breadth of the research. Ultimately, the gathered information is compiled into a detailed report and automatically uploaded to a cloud management platform, achieving systematic organization and sharing of materials, significantly enhancing research efficiency and quality.

This workflow is designed to automate fact-checking in text by utilizing natural language processing technology to split the input text into sentences and verify the authenticity of each one. By invoking a locally running customized language model, it efficiently identifies false information, reduces the workload of manual proofreading, and enhances the accuracy and efficiency of content review. It is suitable for fields such as media, research, and content creation, helping users ensure the authenticity and authority of information, and enabling rapid fact screening and error correction.

This workflow automatically generates optimized web search queries through intelligent semantic analysis and multi-chain thinking, and calls the Brave Search API to obtain relevant results. It is capable of deeply reordering search results and extracting information based on the user's true intent, filtering out the top 10 most relevant high-value links to help users quickly locate the answers they need. It supports Webhook triggers and is applicable in various scenarios such as scientific research, market research, and corporate decision-making, significantly enhancing the relevance and effectiveness of information retrieval.

This workflow provides real-time consultation and assistance through intelligent dialogue and multi-tool collaboration, aiming to enhance users' learning and usage efficiency on the automation platform. It intelligently receives user inquiries, analyzes issues, and automatically retrieves relevant tools and content to generate clear, actionable responses. This helps solve users' challenges in understanding functions and operational guidance, making it suitable for beginners, advanced users, corporate support teams, and training scenarios.

This workflow automates the processing and analysis of financing pitch materials for startups. It detects and downloads PDF files from the Airtable database, uses an AI vision model to transcribe the content into a structured Markdown format, and extracts key information to generate reports. Finally, the data is written back to Airtable and a vector database is constructed, enabling team members to perform natural language queries, significantly enhancing the efficiency of processing financing materials and the convenience of information retrieval.