AI Personal Assistant Suite

A modular collection of AI-powered tools for personal information management and research. This suite includes both a Personal RAG System for querying personal documents and a Web Search Agent for real-time information retrieval.

📁 Project Structure

web_and_rag/
├── agent_web_search/
│    ├── agent_web_search.py         # Web search agent implementation
│    └── README.md                   # Agent Web Search documentation
├── personal-rag-system/
│    ├── me/
│    │    └── summary.txt            # Personal introduction and background
│    ├── rag_multi-docs.py           # Main RAG system implementation
│    └── README.md                   # Personal RAG System documentation
├── hybrid-rag-system/
│    ├── hybrid_rag.py               # Hybrid system combining both approaches
│    └── README.md                   # Hybrid RAG System documentation
├── requirements.txt                 # Python dependencies
├── .env                            # Environment variables (create this)
└── README.md                       # This file

🎯 Overview

This modular AI suite provides three complementary approaches to information retrieval and question answering:

🏠 Personal RAG System

Query your personal documents using advanced Retrieval-Augmented Generation (RAG) technology. Perfect for answering questions about your background, experience, and personal information stored locally.

Use Cases:

• Interview preparation
• Personal information lookup
• Background summaries
• Skills and experience queries

🌐 Agent Web Search

Real-time web search agent that finds current information online using DuckDuckGo. Ideal for research and staying up-to-date with latest developments.

Use Cases:

• Current news and trends
• Research assistance
• Real-time information lookup
• Market research

🔄 Hybrid RAG System

Intelligently combines both local personal documents and web search to provide comprehensive answers. Automatically determines when to use local data, web search, or both.

Use Cases:

• Complex queries requiring both personal and public information
• Professional research with personal context
• Comprehensive question answering
• Adaptive information retrieval

🧠 How They Work Together

Independent Operation: Each system works standalone for specific use cases Hybrid Intelligence: The hybrid system automatically routes queries to the most appropriate source(s) Modular Design: Use any combination based on your needs

🚀 Quick Start

Prerequisites

• Python 3.8+
• OpenAI API key

Installation

1. Clone the repository

   git clone <repository-url>
   cd agent_web_rag

2. Install dependencies

   pip install -r requirements.txt

3. Set up environment variables

   # Create .env file in project root
   echo "OPENAI_API_KEY=your_openai_api_key_here" > .env

4. Set up Personal RAG System

   cd personal-rag-system
   mkdir -p me
   # Add your summary.txt (and optionally other documents) to the me/ folder
   python rag_multi-docs.py

5. Try Web Search Agent

   cd ../agent_web_search
   python agent_web_search.py

📦 Dependencies

All required packages are listed in the shared requirements.txt:

# Core AI/ML Dependencies
openai>=1.12.0
chromadb>=0.4.22
python-dotenv>=1.0.0

# RAG System Dependencies
PyPDF2>=3.0.1
pypdf>=4.0.1
pdfplumber>=0.10.0

# Web Search Dependencies
duckduckgo-search>=6.0.0
agents>=0.1.0

# Optional: Development Tools
pytest>=7.4.0
black>=23.0.0
flake8>=6.0.0

🛠 Usage

Personal RAG System

Navigate to the personal-rag-system/ folder and run:

cd personal-rag-system
python rag_multi-docs.py

Sample Questions:

• "Tell me about my background"
• "What are my main technical skills?"
• "What is my education background?"

Web Search Agent

Navigate to the agent_web_search/ folder and run:

cd agent_web_search
python agent_web_search.py

Sample Queries:

• Latest developments in AI
• Current market trends
• Recent news about specific topics

Hybrid RAG System

Navigate to the hybrid-rag-system/ folder and run:

cd hybrid-rag-system
python hybrid_rag.py

Sample Queries:

• "Who is the CEO of OpenAI and what is their background?" (triggers web search)
• "Tell me about my background" (uses local documents)
• "What are my skills and current AI trends?" (combines both sources)

⚙️ Configuration

Environment Variables

Create a .env file in the project root:

OPENAI_API_KEY=sk-your-openai-api-key-here

Customization

Each system can be customized independently:

• RAG System: Modify document processing, chunking, or response generation
• Web Agent: Adjust search parameters, result count, or search regions

💰 Cost Considerations

RAG System Costs

• Setup: ~$0.001 (one-time document embedding)
• Per Query: ~$0.005 (embeddings + LLM response)

Web Search Costs

• Per Query: ~$0.01-0.02 (LLM processing of search results)

Hybrid RAG Costs

• Local Only Queries: Same as RAG system (~$0.005)
• Hybrid Queries: RAG + Web costs (~$0.015-0.025)
• Smart Routing: Minimizes unnecessary web searches

Monthly Estimate: $1-15 for regular personal use across all systems

Cost Optimization Tips

• Use gpt-4o-mini instead of gpt-4o for 90% cost reduction
• Implement response caching for frequent queries
• Adjust max_tokens to control response length

🔧 Development

Project Structure Benefits

✅ Modular Design: Each system is independent and can be developed separately
✅ Shared Dependencies: Common packages in single requirements.txt
✅ Easy Maintenance: Update or modify systems without affecting others
✅ Scalable: Easy to add new AI agents or tools
✅ Flexible Usage: Use individual systems or combine via hybrid approach
✅ Smart Routing: Hybrid system automatically chooses optimal sources

Adding New Components

1. Create new folder: new-agent/ in the agent_web_rag/ directory
2. Add implementation file and README.md
3. Update main README.md with new component info
4. Add any new dependencies to shared requirements.txt

🚨 Troubleshooting

Common Issues

API Key Not Found

# Ensure .env file is in project root
echo "OPENAI_API_KEY=your_key_here" > .env

Module Not Found

# Install all dependencies
pip install -r requirements.txt

RAG System: Documents Not Found

# Create and populate documents folder
cd personal-rag-system
mkdir -p me
# Add summary.txt (and optional documents)

Web Search: No Results

# Check internet connection and try again
# DuckDuckGo search may have rate limits

🔒 Security & Privacy

• Personal Documents: Remain local, never uploaded to external services
• API Keys: Stored in local .env file, not committed to version control
• Web Searches: Performed through DuckDuckGo (privacy-focused)
• Data Processing: All processing happens locally or through OpenAI API

📈 Performance Tips

RAG System

• Use persistent ChromaDB storage for faster startup
• Implement document caching
• Optimize chunk sizes for your content

Web Search Agent

• Adjust search result count based on needs
• Implement result caching for frequent queries
• Use specific search queries for better results

🤝 Contributing

Development Setup

1. Fork the repository
2. Create feature branch: git checkout -b feature-name
3. Make changes in appropriate module folder
4. Test both systems independently
5. Update relevant documentation
6. Submit pull request

Code Style

• Follow PEP 8 guidelines
• Add docstrings to new functions
• Include error handling
• Update README files for any new features

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• OpenAI for providing powerful AI models
• ChromaDB for excellent vector database
• DuckDuckGo for privacy-focused web search
• Python community for robust ecosystem

📞 Support

For questions or issues:

1. Check the individual README files in each module
2. Review troubleshooting section above
3. Check OpenAI API documentation
4. Create an issue in the repository

---

Built with ❤️ using cutting-edge AI technology for personal productivity# web_rag_search