A terminal-based chat application that leverages OpenAI's GPT-4 model to facilitate long-form conversations. It is designed with a focus on efficient token usage and robust context retention, ensuring cost-effective interactions without compromising the continuity and coherence of conversations.

1. Features
2. Installation
3. Contributions
4. Future Improvements
5. License

• Dynamic Context Management: Automatically manages conversation context to fit within token limits while retaining essential information.
• Token-Efficient Communication: Monitors and controls token usage to minimize API costs.
• Contextual Summarization: Utilizes NLP techniques to summarize lengthy conversation histories into concise context.
• User Intent Tracking: Keeps track of the conversation's direction for relevant context inclusion.
• Cost and Token Analytics: Provides real-time token usage and cost estimates for transparency.
• Persistent Conversation State: Allows conversations to be saved and resumed, maintaining continuity over multiple sessions.

• Clone the repository: git clone https://github.com/siddhant-vij/Efficient-Chat-AI.git
• Navigate to the project directory: cd Efficient-Chat-AI
• Create the conda environment: conda create --name chat-ai python=3.10.13
• Activate the environment: conda activate chat-ai
• Create a .env file in the project root directory based on the .env.example template.
• Install the package: pip install .
• Run the application: chat-ai

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch:

   git checkout -b feature/AmazingFeature

3. Commit your Changes:

   git commit -m 'Add some AmazingFeature'

4. Push to the Branch:

   git push origin feature/AmazingFeature

5. Open a Pull Request

• Interactive Chat UI: Develop a more interactive and user-friendly graphical user interface (GUI) for the chat application. This could include features like conversation threading, real-time response updates, and support for multimedia content.
• Multi-Language Support: Expand the application to support multiple languages, allowing users from different linguistic backgrounds to interact with the AI in their native language.
• Voice Recognition and Synthesis: Integrate voice recognition for input and text-to-speech technologies for output. This would allow users to have spoken conversations with the AI, making the experience more accessible and engaging.
• Advanced Context Management: Implement more sophisticated algorithms for context management that can handle even longer conversations without losing coherence. This might include AI-driven summarization techniques and context prioritization based on user behavior.
• User Personalization: Introduce user profiles that allow for personalized experiences based on past interactions, preferences, and user-specific data.
• Open Source Models: Explore possibilities for replacing the OpenAI API with other Open Source Models & set it all up locally - could be packaged as an offline mode feature.

Distributed under the MIT License. See LICENSE for more information.