ihssane14/Rihla-AI-Morocco-Travel-Assistant

ihssane14/Rihla-AI-Morocco-Travel-Assistant # 🇲🇦 Rihla – AI-Powered Morocco Travel Assistant Rihla is an AI-powered travel assistant that helps users explore Morocco by asking natural language questions. It uses semantic search and Retrieval-Augmented Generation (RAG) to provide accurate and contextual travel recommendations. # Features AI chat for Morocco travel questions Semantic search using vector embeddings Travel itinerary generator FastAPI backend Pinecone vector database HuggingFace LLM (Mistral) # How It Works (Architecture) The user asks a question from the frontend The backend converts the question into a vector Pinecone retrieves the most relevant destinations The AI model generates a response using retrieved data The answer is returned to the user This follows the RAG (Retrieval-Augmented Generation) approach. # Tech Stack Backend: FastAPI (Python) Embeddings: SentenceTransformers (all-MiniLM-L6-v2) Vector Database: Pinecone LLM: Mistral-7B (HuggingFace) Data: JSON (Morocco destinations) # Project Structure RIHLA/ │── main.py │── app.py │── config.py │── create_embeddings.py │── morocco_destinations.json │── requirements.txt │── .gitignore │── README.md # Installation & Setup # Clone the repository git clone github.com cd rihla-ai-travel-assistant # Install dependencies pip install -r requirements.txt # Create a .env file PINECONE_API_KEY=your_key_here HUGGINGFACE_API_KEY=your_key_here PINECONE_INDEX_NAME=rihla-morocco # Run the Project python main.py API available at: 127.0.0.1 Docs: 127.0.0.1 # Key Concepts Used Semantic Search Vector Embeddings Retrieval-Augmented Generation (RAG) REST API # Academic Purpose This project was developed as an educational AI project to demonstrate how semantic search and AI can be combined to build intelligent applications.