Aussist App

Aussist is a mobile application designed to help immigrants in Australia access essential services and information quickly and easily. Built AI Agent App with **Expo (React Native) & LLMs **, the app features useful tools such as emergency contacts, healthcare services, translation support, and more — all in one place.

File Structure

This project combines:

• React Native (Expo) for the mobile UI
• A Python AI Chatbot backend (agent + API)
• RAG pipeline with a vector database (Astra DB) to retrieve relevant knowledge before generating answers

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Key Features

🤖 AI Agent Chatbot (Backend)

• Agent-driven conversation flow (tool-like logic / orchestration via agent_model.py)
• RAG-based answering: retrieves relevant information from a vector database first, then responds
• API-ready design to connect with the mobile app UI

📚 RAG (Retrieval-Augmented Generation)

• Document ingestion pipeline for building a knowledge base
• Vector search retrieval to reduce hallucination and improve factual grounding
• Astra DB integration for storing embeddings + retrieval

📱 Mobile App (Frontend)

• Clean Expo app structure (tabs + screens)
• Built to integrate chatbot responses into the UX
• Designed for real-world immigrant support use cases

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High-Level Architecture

1. User asks a question in the app
2. Mobile app calls the Python API
3. API triggers the AI Agent
4. Agent uses RAG retrieval (Astra DB vector search)
5. Agent generates an answer using retrieved context
6. App displays the response and action based on Agent's Answers

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aussist/
├── app/                      # Main application code (Expo Router)
│   ├── (tabs)/               # Tab-based navigation screens
│   │   ├── _layout.tsx       # Tab navigation configuration
│   │   ├── index.tsx         # Home screen
│   │   ├── emergency.tsx     # Emergency services screen
│   │   ├── healthcare.tsx    # Healthcare services screen
│   │   ├── translation.tsx   # Translation services screen
│   │   └── profile.tsx       # User profile screen
│   ├── components/           # Reusable components
│   │   ├── ui/               # UI component library (Shadcn-inspired)
│   │   │   ├── Button.tsx    # Button component
│   │   │   └── Card.tsx      # Card component
│   │   ├── Header.tsx        # App header component
│   │   └── OnboardingPage.tsx # Onboarding screen component
│   ├── lib/                  # Shared utilities and helpers
│   │   ├── utils.ts          # Utility functions
│   │   └── theme.ts          # Theme configuration
│   ├── index.tsx             # Entry point / Splash screen
│   ├── onboarding.tsx        # Onboarding flow
│   ├── _layout.tsx           # Root navigation layout
│   └── +not-found.tsx        # 404 page
├── chatbot/ # Python AI Agent + RAG backend
│ ├── agent_model.py # AI Agent logic (orchestration + response generation)
│ ├── api.py # API routes / handlers (backend entrypoints)
│ ├── app.py # Backend app runner (server start)
│ ├── astra_db.py # Astra DB connector / DB utilities
│ ├── ingest.py # Data ingestion pipeline → embeddings → Astra DB
│ ├── rag_astradb.py # RAG retrieval logic (query → retrieve context)
│ ├── requirements.txt # Python backend dependencies
│ └── pycache/ # Python cache files
├── .env.example # Environment variable template
├── package.json # Frontend dependencies
├── assets/                   # Static assets (images, fonts)
│   └── images/               # Image assets
├── babel.config.js           # Babel configuration
└── tailwind.config.js        # Tailwind CSS configuration

UI Components

The app uses a library of reusable UI components inspired by Shadcn UI:

• Button: Versatile button component with variants (default, destructive, outline, etc.) and sizes
• Card: Flexible card component with header, content, title, description, and footer sections

Screens

• Home: Main landing page with language selection and service categories
• Emergency: Quick access to emergency contacts and symptom checker
• Healthcare: Find nearby hospitals and healthcare facilities
• Translation: Translation tools and services
• Profile: User profile management and settings

Getting Started

1. Install dependencies:

   npm install
   

2. Start the development server:

   npx expo start
   

3. Backend (Python Chatbot)

 cd chatbot
 python -m venv venv
 source venv/bin/activate     # macOS / Linux
 venv\Scripts\activate        # Windows
 pip install -r requirements.txt

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RAG Pipeline Explained (What the chatbot folder does)

ingest.py — Build the knowledge base

Takes documents / knowledge sources

Converts them into embeddings

Stores them in Astra DB as vectors for retrieval

rag_astradb.py — Retrieve context for a question

Accepts a user query

Searches Astra DB vectors to find the most relevant chunks

Returns contextual passages to ground the AI response

agent_model.py — AI Agent orchestration

Receives the user message

Calls the retrieval module (RAG)

Builds a final answer using both:

user question

retrieved context

api.py / app.py — API layer

Exposes chatbot endpoints

Connects frontend ↔ agent ↔ database

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Styling

Access this link for viewing the demo and guide since the size of demo video is too big! https://drive.google.com/drive/folders/1RgBwmBP6o3-1CpreVCB5JIAxL_rSoNit?usp=sharing