Gemini with Memory: A Powerful AI Chatbot with Enhanced Personalisation

Gemini with Memory is an advanced AI chatbot built upon Google's Gemini model, augmented with a sophisticated memory system. This system allows the chatbot to retain some specific usefuyl information about user locally from past interactions, providing a more personalized and contextually relevant conversational experience.

Features

• Dual Memory System: Employs a dual memory system comprising:
  • Permanent Memory: Stores user preferences, important facts, and frequently used information for long-term personalization.
  • Time-Based Memory: Stores time-sensitive information like reminders and scheduled events, automatically expiring outdated entries.
• Automatic Data Extraction: Extracts relevant information from user input using a dedicated LLM and saves it to the appropriate memory type.
• Memory Compression: Periodically compresses memory by deduplicating and merging entries using an LLM, ensuring efficient storage and retrieval.
• Conversation Management: Supports starting new conversations, loading previous ones, and deleting conversations.
• Rich User Interface: Provides a user-friendly interface with clear menus, conversation history, and formatting options.

Real-Life Use Cases

• Personalized Assistants: Create customized AI assistants that remember user preferences, habits, and important information, providing tailored support and recommendations.
• Interactive Storytelling: Develop immersive interactive stories where the AI remembers past events and character details, creating a dynamic and engaging narrative.
• Educational Tools: Build AI tutors that track student progress, adapt to individual learning styles, and provide personalized feedback.
• Customer Service Bots: Implement intelligent customer service bots that recall past interactions and customer details, offering efficient and personalized support.
• Research Assistants: Create AI-powered research assistants that can store and retrieve relevant information from various sources, aiding in literature reviews and data analysis.

Getting Started

1. Clone the repository:

   git clone https://github.com/KingNish24/gemini-with-memory.git

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file and set the GEMINI_API_KEY in it.
4. Run the chatbot:

   python run.py

Future Enhancements

• Multimodal Memory: Extend the memory system to store and process various data types, including images, audio, and video.
• Improved Memory Search: Implement more sophisticated search algorithms for efficient retrieval of information from memory.
• User-Defined Memory Tags: Allow users to tag memory entries with custom labels for better organization and retrieval.
• Integration with External Services: Integrate with external services like calendars, task managers, and knowledge bases to enhance the chatbot's capabilities.

Contributing

Contributions are welcome!

License

This project is licensed under the MIT License.