AskSitare

AskSitare is an intelligent chatbot web application designed specifically for Sitare University. It uses natural language processing and semantic search to provide accurate responses to university-related queries.

Features

• Real-time chat interface for student queries
• Semantic search using sentence transformers
• Integration with Groq's LLaMA 3.1 70B model for generating responses
• Feedback system for response quality tracking
• Admin dashboard for monitoring feedback
• PostgreSQL database for storing questions and feedback
• Vector similarity search for finding relevant responses

Technologies Used

• Backend: Flask (Python)
• Database: PostgreSQL with psycopg2
• AI/ML:
  • Groq API for LLM integration
  • Sentence Transformers for text embeddings
  • LLaMA 3.1 70B model for response generation
• Frontend: HTML/Templates

Installation

1. Clone the repository
2. Install the required dependencies:

pip install flask psycopg2-binary groq sentence-transformers

3. Set up environment variables:

export GROQ_API_KEY="your_groq_api_key"
export PORT=5000  # Optional, defaults to 5000

4. Configure the database connection in the code by updating DB_PARAMS:

DB_PARAMS = {
    "dbname": "your_db_name",
    "user": "your_db_user",
    "password": "your_db_password",
    "host": "your_db_host",
    "port": 5432
}

Usage

1. Start the server:

python app.py

2. Access the chatbot interface at http://localhost:5000

3. For admin access:

   • Navigate to /login
   • Use the admin credentials to access the dashboard
   • View user feedback and interaction data

Features in Detail

Semantic Search

• Uses sentence transformers to convert questions into embeddings
• Performs vector similarity search to find relevant matches
• Returns top 5 most similar questions and their associated answers

Chat Interface

• Real-time response generation
• Streaming responses for better user experience
• Feedback system for users to rate responses

Admin Dashboard

• Secure login system
• View and analyze user feedback
• Monitor chatbot performance

Database Schema

The application uses the following main tables:

• questions: Stores questions and their embeddings
• topics: Stores detailed answers/paragraphs
• feedback: Stores user feedback on responses

Security

• Secure admin login system
• Environment variable based configuration
• Database connection security
• Session management for admin access

Contributing

Please follow these steps to contribute:

1. Fork the repository
2. Create a new branch for your feature
3. Submit a pull request with a clear description of your changes

ML Analysis

ML Analysis repo👉 @ https://github.com/deepalitomar021