My System

An intelligent, self-learning Telegram userbot powered by Google's Gemini AI and a persistent MySQL database for long-term memory.

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About The Project

My System is not just a standard userbot; it's a digital extension of the user. It actively listens to conversations it's part of, learns facts about the user, and understands their opinions on various topics. By leveraging a powerful AI model and a structured database, it builds a long-term memory, allowing it to reply to mentions with context, personality, and knowledge that grows over time.

The core philosophy is to create an AI assistant that is indistinguishable from a real person, with an adaptive, witty, and intelligent persona.

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Key Features

• 🧠 AI with Persistent Memory: Learns new facts and opinions from conversations and stores them in a MySQL database, ensuring knowledge is never lost.
• 💬 Context-Aware Replies: Responds intelligently when mentioned by using both short-term (recent chat history) and long-term (facts and opinions) memory.
• 🎭 Adaptive Persona: Dynamically adjusts its response tone based on the context of the query—behaving as a helpful assistant for factual questions, a witty companion for casual chats, and a sharp defender against insults.
• 📜 Advanced Chat Summarization: Quickly get the gist of any conversation with the powerful .summarize command, which can analyze recent messages, unread messages, or messages from a specific user.
• 🔒 Secure & Private: Operates as a user account (userbot), giving you full control over its actions and ensuring your data and conversations remain private.

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Technology Stack

• Core: Python 3.9+
• Telegram API: Telethon
• AI Model: Google Gemini
• Database: MySQL (via aiomysql)
• Configuration: python-dotenv

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Setup and Installation

Follow these steps to get your instance of My System up and running.

Prerequisites

• Python 3.9 or higher.
• A running MySQL-compatible database (e.g., local MySQL, PlanetScale, Aiven).
• Your Telegram API_ID and API_HASH. Get them from my.telegram.org.
• A Google API Key for Gemini. Get one from Google AI Studio.

Installation Steps

1. Clone the repository:

   git clone https://github.com/umaarov/my-system.git
   cd my-system

2. Install the required packages:

   pip install -r requirements.txt

3. Configure your environment variables: Create a file named .env in the project root and populate it with your credentials. You can use the example below as a template.

   # .env file
   
   # --- Telegram Credentials ---
   API_ID=1234567
   API_HASH=your_api_hash_here
   
   # --- Google Gemini AI ---
   GOOGLE_API_KEY=your_google_api_key_here
   
   # --- Database Connection ---
   DB_HOST=your_database_host
   DB_PORT=3306
   DB_USER=your_database_username
   DB_PASS=your_database_password
   DB_NAME=your_database_name

4. Set up your user identity: Open config.py and change the MY_USERNAME and MY_ALIASES to match your own Telegram username and real names/nicknames. This is crucial for the bot to know when a message is about you.

   # config.py
   MY_USERNAME = "your_telegram_username"
   MY_ALIASES = ["your_first_name", "your_last_name", "your_nickname"]

5. Initialize the database: Run the setup script once to create all the necessary tables in your database.

   python setup_database.py

   You should see a success message indicating the tables are ready.

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Usage

To start the userbot, simply run the main script:

python userbot.py

The first time you run it, Telethon will ask you to log in with your phone number, password, and 2FA code in the console. After a successful login, a userbot.session file will be created, and subsequent runs will be automatic.

Commands

• Summarize Chat: Use the .summarize command in any chat.
  • .summarize 50: Summarizes the last 50 messages.
  • .summarize @username: Summarizes the last 50 messages from a specific user.
  • .summarize unread: Summarizes all unread messages in the chat.

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Database Schema

The bot's "memory" is structured across four main tables:

• entities: Stores unique nouns identified in conversations (e.g., people, places, topics).
• facts: Stores specific attributes about entities, particularly the user (e.g., (entity: dribbblxr, attribute: location, value: Uzbekistan)).
• user_opinions: Stores the user's sentiment (positive, negative, neutral) towards different entities.
• conversation_history: Acts as the short-term memory, storing recent messages from chats for contextual replies.