🎯 Guard-Railed Support Ticket Copilot

A guard-railed AI-powered support ticket processing system that combines FastAPI backend services with a Streamlit frontend interface. This project demonstrates a complete pipeline for processing IT support tickets with built-in safety guardrails, authorization checks, and RAG-based resolution.

📋 Table of Contents

• Features
• Architecture
• Prerequisites
• Installation
• Configuration
• Running the Application

✨ Features

• Guard-Railed Processing: Built-in safety checks to ensure appropriate ticket handling
• Authorization Engine: Role-based access control for sensitive operations
• RAG-Based Resolution: Retrieval-Augmented Generation for intelligent ticket responses
• Appropriateness Classification: Filters out off-scope or inappropriate requests
• Employee Lookup: Validates employee information and permissions
• RESTful API: Clean FastAPI backend with automatic documentation
• Interactive UI: User-friendly Streamlit frontend interface

🏗️ Architecture

┌─────────────────────┐     HTTP      ┌─────────────────────┐
│   Streamlit UI      │ ────────────► │   FastAPI Backend   │
│  (streamlit_simple) │   Requests    │      (main.py)      │
└─────────────────────┘               └──────────┬──────────┘
                                                 │
                                                 ▼
                                      ┌─────────────────────┐
                                      │   Ticket Pipeline   │
                                      │   (ticket_graph)    │
                                      └──────────┬──────────┘
                                                 │
                    ┌────────────────────────────┼────────────────────────────┐
                    │                            │                            │
                    ▼                            ▼                            ▼
         ┌──────────────────┐       ┌──────────────────┐       ┌──────────────────┐
         │  Appropriateness │       │  Authorization   │       │   RAG Engine     │
         │   Classifier     │       │  Rules Engine    │       │ (support_ticket) │
         └──────────────────┘       └──────────────────┘       └──────────────────┘

📦 Prerequisites

• Python 3.9 or higher
• pip (Python package manager)
• Azure OpenAI API access (for LLM-powered features)

🛠️ Installation

1. Clone the repository

   git clone https://github.com/kasaxena5/ai_hackathon.git
   cd ai_hackathon

2. Create and activate a virtual environment

   python -m venv .venv
   .venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

⚙️ Configuration

Update the .env file in the project root directory with the api key:

AZURE_OPENAI_API_KEY=your_api_key_here

🚀 Running the Application

You need to run both the backend and frontend services:

1. Start the FastAPI Backend

python main.py

The API will be available at http://localhost:8000

2. Start the Streamlit Frontend

In a separate terminal (with the virtual environment activated):

streamlit run streamlit_simple.py

The UI will open automatically in your browser.