MusafirAI: RAG-Powered Travel Itinerary Generator

🧳 Project Overview

MusafirAI transforms travel planning through Retrieval-Augmented Generation (RAG) technology, creating personalized Pakistan itineraries by combining AI with curated travel knowledge. Built with LangChain and Cohere/Gemini AI, this system converts travel guides into intelligent recommendations with source attribution, delivering culturally rich travel plans.

🔧 Core Functionality

MusafirAI solves the problem of generic travel planning by implementing:

• Destination Intelligence: Semantic search across curated Pakistan travel documents
• Personalized Itineraries: Day-by-day plans based on budget, interests, and group size
• Contextual Recommendations: Culturally appropriate suggestions with practical info
• Knowledge Expansion: Upload custom travel guides to enhance recommendations
• Source Attribution: Show sources used for each itinerary component

🏗️ Technical Architecture

System Architecture Diagram

System Components

1. Document Processing Engine(document_loader.py):

• DirectoryLoader with text chunking (600 tokens, 100 overlap)
• Metadata enrichment for locations (city, province, type)

2. Vector Intelligence Core (vector_store_manager.py):

• ChromaDB/FAISS vector stores with SentenceTransformer embeddings
• Automatic persistence and recreation of knowledge base

3. RAG Orchestrator (rag_system.py):

• RetrievalQA chain with MMR search and score filtering
• Custom prompt engineering for structured itineraries
• Dual LLM support (Cohere Command/Gemini Pro)

4. Web Interface (MusafirAI.py):

• Streamlit UI with PDF export and source inspection
• Dynamic form handling for travel preferences
• Vector store management controls

Technology Stack

• AI Framework: LangChain 0.0.340
• LLMs: Cohere Command, Google Gemini Pro
• Embeddings: all-MiniLM-L6-v2 Sentence Transformers
• Vector DB: ChromaDB 0.4.15 / FAISS 1.7.4
• Web Framework: Streamlit 1.28.1
• Dependencies: Python 3.11, ReportLab, BeautifulSoup

🚀 Key Features & Innovations

Advanced RAG Implementation

• Destination-aware chunking preserving location context
• Hybrid metadata filtering (city/province/type) + semantic search
• Prompt engineering enforcing day-by-day structure with practical details

User Experience Excellence

• Interactive preference form with Pakistan-specific options
• Source document inspection with metadata
• One-click PDF/text itinerary downloads
• Real-time vector store management

Production-Ready Features

• API key management with .env and Streamlit Secrets
• Automatic directory creation for cloud environments
• Comprehensive error handling with user feedback
• Dual deployment support (local/cloud)

📊 Performance & Validation

System Workflow

System Capabilities

• Processes 50+ page documents in under 60 seconds
• Generates 7-day itineraries with 5+ attractions/day
• Maintains context across multiple queries
• Accurately attributes sources with content previews

Validation Metrics

• Processes 50+ page documents in under 60 seconds
• Generates 7-day itineraries with 5+ attractions/day
• Maintains context across multiple queries
• Accurately attributes sources with content previews

🛠️ Implementation Details

Installation & Setup

git clone https://github.com/zshafique25/RAG_Assisstant.git
cd RAG_Assisstant

# Install dependencies
pip install -r requirements.txt

# Add API keys to .env
echo "COHERE_API_KEY=your_key" >> .env
echo "GEMINI_API_KEY=your_key" >> .env

# Launch app
streamlit run MusafirAI.py

Configuration Requirements

• Cohere or Gemini API key
• Travel documents in travel_documents/ directory
• Python 3.11 (via runtime.txt)

Prompt Engineering

PROMPT_TEMPLATE = """Create Pakistan travel itinerary using context:

Context Information:
{context}

User Query: {question}

Instructions:
1. Create day-by-day itinerary
2. Include key attractions from context
3. Add practical info: travel times, costs
4. Suggest accommodation and dining
5. Include cultural etiquette and safety tips
6. Keep concise and realistic"""

🎯 Use Cases & Applications

Tourism Industry

• Travel agencies generating personalized packages
• Hotel chains creating neighborhood guides
• Tourism boards promoting regional destinations

Traveler Applications

• Solo travelers discovering hidden gems
• Families planning multi-generational trips
• Adventure seekers finding offbeat experiences

Educational Use

• Cultural studies students exploring regions
• Geography classes analyzing travel patterns
• Language learners exploring local dialects

🔬 Technical Innovations

RAG Pipeline Optimization

• Dynamic chunk sizing based on content type
• Hybrid retrieval (similarity + metadata filtering)
• LLM fallback mechanism (Cohere → Gemini)

Knowledge Management

• Automatic metadata extraction from filenames
• User-uploaded document processing pipeline
• Vector store versioning through force_recreate

Cloud Optimization

• Ephemeral storage handling for serverless environments
• Dependency resolution for Python 3.11
• Secret management with Streamlit TOML

📈 Future Enhancements

Immediate Roadmap

• 🗺️ Interactive Pakistan map for destination selection
• 💬 Chat interface for itinerary modifications
• 📊 Budget calculator with real-time pricing

Long-Term Vision

• Multi-format support (PDF/Word/YouTube transcripts)
• Hotel/activity booking API integration
• Local language support (Urdu/Pashto)
• AR experience for destination previews

🏆 Project Impact & Value

Technical Contributions

• Complete open-source RAG travel assistant
• Production-ready cloud deployment configuration
• Modular architecture for easy extension

Practical Benefits

• Democratizes travel expertise through AI
• Preserves cultural knowledge in retrievable format
• Increases tourism accessibility for remote regions

Innovation Recognition

• Dual LLM support with failover mechanism
• Context-aware metadata enrichment
• Cloud-native vector store management

🎓 Learning Outcomes & Skills Demonstrated

AI Engineering

• End-to-end RAG system implementation
• Vector database management/optimization
• LLM integration and prompt engineering

Software Development

• Modular architecture with separation of concerns
• Streamlit UI development with custom CSS
• PDF generation with ReportLab

DevOps & Production

• Cloud deployment on Streamlit Community Cloud
• Dependency management for constrained environments
• Secret management and security practices

📋 Conclusion

MusafirAI represents a cutting-edge application of RAG technology to revolutionize travel planning for Pakistan. By combining semantic search with generative AI, it delivers personalized, culturally-rich itineraries grounded in verified travel knowledge. The project demonstrates professional-grade architecture with production-ready error handling, cloud deployment, and user-friendly interfaces.

The system's modular design allows easy extension to new regions or languages, while its open-source nature provides a valuable resource for developers exploring RAG implementations. MusafirAI stands as a testament to how AI can transform traditional industries while preserving cultural authenticity.

Repository: github.com/zshafique25/RAG_Assisstant

Live Demo: Rag_assisstant

Documentation: Comprehensive setup/usage instructions in README.md