MindEase AI Integration

AI-powered chatbot integration for MindEase, a mental health and emotional well-being support platform for students.

Overview

This service provides an intelligent chatbot that helps students navigate academic stress and emotional challenges. It integrates with the main MindEase application via a REST API, offering multi-turn conversations with conversation history persistence.

Key Features:

• Empathetic AI Companion: Designed specifically to support students with academic stress, anxiety, time management, and emotional well-being
• Multi-turn Conversations: Maintains conversation history for contextual, natural discussions
• REST API: Easy integration with web and mobile clients
• Conversation Management: Create, retrieve, and manage user conversations
• Production-Ready: Deployed with Docker and health checks

Architecture

• Backend: FastAPI REST API (port 8000)
• AI Model: Groq API with Llama 3.3 70B
• Database: SQLite with conversation and message persistence
• UI (Dev): Chainlit web interface (port 8001) for testing and development

Quick Start (Docker)

Prerequisites

• Docker and Docker Compose
• Groq API key (get one at console.groq.com)

Setup

1. Clone the repository:

git clone https://github.com/DejusDevspace/MindEase-AI-Integration.git
cd MindEase-AI-Integration

2. Create .env file:

cp .env.example .env

3. Add your Groq API key to .env:

GROQ_API_KEY=your_groq_api_key_here
GROQ_MODEL=llama-3.3-70b-versatile
MAX_TOKENS=500
TEMPERATURE=0.7
DEBUG=false

4. Start the service:

docker-compose up

The API will be available at http://localhost:8000 with interactive docs at http://localhost:8000/docs.

Local Development

Prerequisites

• Python 3.13+
• uv package manager (or pip)

Setup

1. Clone the repository:

git clone https://github.com/DejusDevspace/MindEase-AI-Integration.git
cd MindEase-AI-Integration

2. Create .env file:

cp .env.example .env
# Add your Groq API key

3. Install dependencies:

uv sync

4. Run the FastAPI server:

python main.py

Or start the Chainlit interface for testing:

python main.py chainlit

API Usage

Chat Endpoint

Send a message and get a response:

curl -X POST "http://localhost:8000/v1/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "user123",
    "content": "I am stressed about my exams",
    "conversation_id": "conv-id-optional"
  }'

Response:

{
  "message": "I hear you—exam stress is really tough...",
  "conversation_id": "conv-uuid",
  "tokens_used": 145
}

Clear Conversation

Remove all messages from a conversation (keeps conversation record):

curl -X DELETE "http://localhost:8000/v1/conversations/{conversation_id}?user_id={user_id}"

Delete Conversation

Delete a conversation and all its messages:

curl -X DELETE "http://localhost:8000/v1/conversations/{conversation_id}/delete?user_id={user_id}"

Health Check

curl http://localhost:8000/health

Configuration

Environment variables in .env:

Variable	Default	Description
GROQ_API_KEY	Required	Your Groq API key
GROQ_MODEL	llama-3.3-70b-versatile	LLM model to use
MAX_TOKENS	500	Maximum response length
TEMPERATURE	0.7	Response creativity (0-1)
DEBUG	false	Debug logging

About MindEase

MindEase began as a capstone project during an internship at FlexiSAF Edusoft Limited, developed by a cross-functional team tasked with creating innovative solutions to support student well-being. The AI integration service was built to provide students with accessible, compassionate support for managing academic stress and emotional challenges.

Contact

For questions or issues, please open an issue on GitHub.