Leadership
Hyper-Intelligent Deep Research Chatbot
This workflow creates detailed research reports based on user prompts. It uses AI to define research areas, searches Tavily for information, generates content, and saves reports to Google Sheets and vector stores for future retrieval, all initiated via Telegram.
Built · ~3 hours saved per week
This workflow is for anyone needing detailed research reports, like students, academics, or business analysts.
This workflow generates comprehensive research reports on a given topic. It uses AI to identify key research areas, find relevant sources, write sections, and create a PDF report. It then stores the report in a Google Sheet and a knowledge base, making it easy to retrieve later via a chatbot.
Set up this workflow by connecting your Telegram, Tavily, OpenAI, DeepSeek, Google Sheets, and Supabase accounts. Most AI models are interchangeable with other models, be sure to match up the AI model to the model you want to use.
You need accounts for Telegram, Tavily (for web search), OpenAI or DeepSeek (for AI language models), Google Sheets, and Supabase (for the knowledge base).
You can customize the depth of research, the number of sources, and the writing style of the AI. You can also change the structure of the Google Sheet, use other AI models or use any vector database you choose by editing the API parameters and prompt instructions in the AI nodes.
Key Technologies: AI (OpenAI, DeepSeek),Tavily (Web Search),Google Sheets,Supabase (Vector Database),Telegram (Chatbot Interface),n8n (Automation Platform),PDFShift (PDF Generation)
Automation Value: Automates in-depth research and report generation. Reduces manual effort in information gathering and synthesis. Creates structured, well-sourced documents. Builds a searchable knowledge base for quick information retrieval. Integrates with Telegram for easy access and interaction.
Best Practices: Ensure all API credentials are securely stored and properly configured. Clearly define AI prompts for consistent and high-quality output, especially for structured data generation and content writing. Regularly monitor the performance of web search (Tavily) to ensure it retrieves relevant and up-to-date sources. Optimize the chunking strategy (Recursive Character Text Splitter) for the vector database to balance retrieval speed and accuracy. Implement error handling and retry mechanisms for external API calls (Tavily, PDFShift, OpenAI/DeepSeek) to improve workflow robustness.
Tags: Chatbot, RAG, Content Management, Content Creation, Google, Telegram