PeakMind Wellbeing Coach

An AI-powered chatbot serving as a personal guide for wellness and productivity. This project showcases practical application of the Gemini API, effective prompt engineering, a local vector database for knowledge retrieval, and a well-structured Retrieval-Augmented Generation (RAG) pipeline.

Features

• 🤖 AI-powered wellbeing and productivity coaching
• 📚 Knowledge base with wellness and productivity content
• 🔍 Semantic search using FAISS vector database
• 👤 User profile and personalization
• 💬 Natural conversation interface
• 🎯 Domain-specific responses

Setup Instructions

1. Clone the repository:

git clone <repository-url>
cd PeakMindWellbeingCoach

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables: Create a .env file in the project root and add your Google API key:

GOOGLE_API_KEY=your_api_key_here

Running the Application

1. Start the Streamlit app:

streamlit run WellbeingCoachApp.py

2. Open your browser and navigate to the URL shown in the terminal (typically http://localhost:8501)

Project Structure

• WellbeingCoachApp.py: Main application file
• knowledge_base.py: Knowledge base content and management
• vector_db.py: Vector database management
• requirements.txt: Project dependencies
• .env: Environment variables (create this file)

RAG Pipeline

The application implements a Retrieval-Augmented Generation (RAG) pipeline:

1. User Query Input

   • User submits a question through the chat interface

2. Query Preprocessing

   • Question is analyzed for user profile updates
   • Semantic search query is prepared

3. Information Retrieval

   • Vector database is searched for relevant content
   • Top-k most similar documents are retrieved

4. Augmented Prompt Construction

   • Retrieved context is combined with system prompt
   • User profile information is included
   • Question is formatted for the model

5. LLM Call

   • Gemini Pro model generates response
   • Response is contextualized with retrieved information

6. Response Post-processing

   • Response is formatted for display
   • User profile is updated
   • Interaction is logged

Knowledge Base

The knowledge base includes information about:

• Productivity techniques (Pomodoro, time blocking)
• Mindfulness and meditation
• Stress management
• Work-life balance
• Personal development

Ethical Considerations

• The chatbot is designed to provide general wellness and productivity advice only
• It is not a replacement for professional medical or mental health services
• Users are advised to consult qualified professionals for serious concerns
• The system includes appropriate disclaimers and redirections

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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