🐾 Pet Care AI - Complete Project

Welcome to Pet Care AI, an AI-powered platform that provides intelligent assistance for pet owners. This project integrates LLM-RAG (Retrieval-Augmented Generation) with FastAPI for the backend and Next.js for the frontend, enabling document-based AI chat functionalities.

📂 Project Structure

Path	Description
pet-care-project/	Root directory of the project
├── pet-care-backend/	Backend (FastAPI, RAG, OpenAI API)
├── pet-care-ai/	Frontend (Next.js, React, MUI)
├── .gitignore	Git ignore file
├── README.md	Main repository documentation

Each folder contains a README.md file with detailed installation steps.

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🏗️ Backend (FastAPI & RAG)

📌 Tech Stack:

• FastAPI – Web framework for API development
• FAISS – Vector search for document-based AI chat
• OpenAI API – Chat-based AI responses
• PyPDF2 – PDF document processing
• Sentence Transformers – Text embedding for document search

📌 Key Features:

• ✅ LLM-RAG Implementation
• ✅ PDF Upload & Processing
• ✅ Vector Search with FAISS
• ✅ AI Chat with Retrieval-Augmented Generation (RAG)

🔗 Backend Documentation & Setup

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🎨 Frontend (Next.js & React)

📌 Tech Stack:

• Next.js – React framework for SSR & client-side rendering
• Material UI (MUI) – Responsive UI components
• Axios – API communication with backend
• React Hooks – State management

📌 Key Features:

• ✅ Chat Interface – AI-powered chat
• ✅ PDF Upload UI – Upload documents for AI analysis
• ✅ Modern UI – MUI-powered responsive design

🔗 Frontend Documentation & Setup

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🚀 Installation & Running the Project

1️⃣ Clone the Repository

git clone https://github.com/T-DevH/pet-care-project.git
cd pet-care-project

2️⃣ 🚀 Set Up the Backend

cd pet-care-backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload

🚀 Backend is now running at: http://127.0.0.1:8000

3️⃣ 🚀 Set Up the Frontend

cd ../pet-care-ai
npm install
npm run dev

🚀 Frontend is now running at: http://localhost:3000

📌 Key API Endpoints

Method	Endpoint	Description
POST	/api/upload-pdf	Upload a PDF for AI processing
POST	/api/ask	Ask questions about the uploaded PDF
POST	/api/chat	Chat with AI assistant

📚 LLM-RAG Implementation & Blog Series

This project is part of a 6-blog series covering LLM-RAG implementation from exploration to enterprise deployment, including AI agents and agentic AI.

📌 Next Steps:

• Upgrade to NVIDIA NeMo + NIMs for optimized cost-effective deployment
• Scale with GPU acceleration

Stay tuned for updates in the blog series!