IntelliChat is a powerful, multi-language conversational AI chatbot that delivers accurate, context-aware responses. Equipped with persistent chat history and customizable model settings, IntelliChat is designed to provide a seamless, user-friendly experience for general knowledge queries and more.

IntelliChat can be deployed on Streamlit for an intuitive and scalable user interface.

• Persistent Chat History: Maintains the flow of conversation across queries.
• Multi-Language Support: Communicate in English, Spanish, French, German, or Hindi.
• Customizable Models: Choose from state-of-the-art models:
  • LLaMA 3.1-8B: Efficient for various language tasks.
  • Gemma2 9B: Offers nuanced and contextually rich responses.
  • Mixtral: Multi-modal capabilities for creative applications.
• Adjustable Parameters: Fine-tune temperature and max tokens to control output creativity and length.
• Real-Time LangChain Tracing: Enhanced debugging and performance optimization.

• LLaMA 3.1-8B: Optimal for general text generation tasks.
• Gemma2 9B: Ideal for nuanced, context-aware responses.
• Mixtral: Suitable for multi-modal (text and visual) tasks.

• Temperature: Controls creativity in responses.
• Max Tokens: Limits the length of generated outputs.

1. User inputs are sent to the chosen model via LangChain.
2. Responses are generated based on the provided language and history context.
3. Persistent chat history ensures a continuous, natural conversation flow.

IntelliChat supports five languages:

• English
• Spanish
• French
• German
• Hindi

1. Clone the repository:

   git clone https://github.com/miteshgupta07/IntelliChat.git
   cd IntelliChat

2. Install Dependencies

   pip install -r requirements.txt

3. Set up API keys

• Add your API keys to repository secrets (for deployment) or .env (for local use).

4. Run the app locallly

   streamlit run app.py

API keys are securely stored as GitHub Repository Secrets or in .env files for local development.

• Streamlit: For an interactive user interface.
• LangChain: To manage prompts and chat history.
• ChatGroq: High-performance conversational models.
• Python: Core programming language for logic and integration.

Contributions are welcome! Please open an issue or submit a pull request for any improvements or feature suggestions.

This project is licensed under the MIT License - see the LICENSE file for details.

• LangChain: For robust history and prompt management.
• Groq Models: For providing state-of-the-art conversational AI capabilities.
• Streamlit Community: For helpful resources and support.

For inquiries or collaborations, please contact me at miteshgupta2711@gmail.com.