Customer Service
Memory Chart + Notes AI Telegram Chatbot
This workflow creates an AI chatbot on Telegram with long-term memory and note-taking capabilities using Baserow for storage and PostgreSQL for chat memory. It processes text, audio, and images to provide personalized, context-aware responses.
Built · ~11.5 hours saved per week
This workflow is for anyone who wants a smart Telegram chatbot that remembers past conversations and important notes. This could be a personal assistant, a customer service bot, or a tool to help you stay organized.
This workflow creates an AI chatbot on Telegram that can understand and respond to messages, including text, voice, and images. It stores memories and notes in Baserow and uses a Postgres database for chat history, making it a personalized and context-aware assistant.
You will need to set up a Telegram bot, Baserow tables for memories and notes, and a Postgres database for chat memory. The workflow includes sticky notes with setup instructions for Baserow. You will also need to configure your OpenAI credentials.
Telegram account, Baserow account, Postgres database, OpenAI API key, n8n instance.
You can customize the AI agent's system message to change its personality and rules. You can also add more tools to the AI agent to expand its capabilities. The Baserow tables can be adapted to store different types of memories and notes.
Key Technologies: Telegram,OpenAI (GPT-4o-mini),Baserow,Postgres,n8n
Automation Value: Provides a personalized and intelligent Telegram chatbot experience. Automates memory and note-taking, reducing manual effort. Enhances user interaction by remembering past conversations and relevant information. Allows for multi-modal input (text, voice, image), making the bot versatile. Offers a scalable solution for managing personal or business information through Baserow and Postgres.
Best Practices: Ensure the Baserow tables are correctly configured with 'Memory' (long text) and 'Date Added' (date with time) fields for both memories and notes. Regularly review and refine the AI Agent's system message to optimize its persona, tool usage, and response quality. Implement reliable error handling and fallback responses to provide a smoother user experience. Monitor the Postgres database for chat memory to ensure it's retaining historical conversations effectively.
Tags: Chatbot, Leadership Automations, RAG, postgres, AI Assistant
Integrations: OpenAI, Telegram, AI Agent, Postgres, Webhook