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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
  1. Set up environment variables:
export GROQ_API_KEY="your_groq_api_key"
export PORT=5000  # Optional, defaults to 5000
  1. 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

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

  2. 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