PAYANA – RAG-Based Travel Assistant

An AI-powered travel planning assistant built using Retrieval-Augmented Generation (RAG).
This system generates personalized Karnataka travel itineraries based on user queries, budget, and number of days.

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🚀 Project Overview

This project combines:

• FAISS Vector Search for semantic retrieval
• Sentence Transformers for embeddings
• Groq LLM (Llama 3.1 8B Instant) for itinerary generation
• ScaleDown API for context compression
• Flask Backend with CORS support
• Unsplash API for destination images

Architecture Flow:

User Query
→ Embedding (SentenceTransformer)
→ FAISS Vector Search
→ Context Compression (ScaleDown)
→ LLM Generation (Groq)
→ Structured JSON Travel Plan

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✨ Features

• Semantic search over Karnataka tourism data
• Budget-aware itinerary generation
• Dynamic day extraction from user query
• Clean JSON enforcement with auto-repair parsing
• Tourism-focused summarization
• Image enrichment using Unsplash API
• Production-ready Flask API

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📦 Tech Stack

• Python 3.10+
• Flask
• FAISS (CPU)
• SentenceTransformers (all-MiniLM-L6-v2)
• Groq API (Llama 3.1 8B Instant)
• ScaleDown API
• Unsplash API

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🔑 Environment Variables

Create a .env file inside the backend folder:

GROQ_API_KEY=your_groq_api_key
SCALEDOWN_API_KEY=your_scaledown_api_key
UNSPLASH_KEY=your_unsplash_key

⚙️ Installation

git clone https://github.com/yourusername/karnataka-rag-travel.git
cd backend
pip install flask flask-cors faiss-cpu numpy requests sentence-transformers python-dotenv

▶️ Running the Server

python app.py

Server runs at:

http://localhost:5000

DEMO VIDEO:

demo_video.mp4