🔍 Advanced RAG Project

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📄 Overview

The Advanced RAG Project (Retrieval-Augmented Generation) is an innovative application that leverages state-of-the-art technologies like FAISS and Gemini to offer a powerful Q&A interface based on Muhammad Fahmi's professional resume. This project integrates retrieval mechanisms with generative AI to provide concise and accurate answers based on a resume database. It’s a cutting-edge example of combining AI embeddings with user-friendly visualizations.

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Table of Contents

1. 🎯 Objectives
2. 🔧 Technologies Used
3. 📝 Directory Structure
4. 🔍 Features
5. 🔄 Project Workflow
6. 🎉 Conclusion
7. 📚 References
8. 📜 License
9. 🗺️ Code Workflow

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🎯 Objectives

• 🚀 Demonstrate advanced RAG workflows by integrating retrieval and generative AI models.
• 🔍 Build an interactive resume Q&A interface for professional inquiries.
• 🎓 Showcase the capabilities of FAISS and Gemini in a real-world application.

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🔧 Technologies Used

Other libraries:

• LangChain: For prompt handling.
• HuggingFace Embeddings: For embedding generation.
• PIL: For image handling.
• Gemini API: For generative content creation.

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📝 Directory Structure

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├── LICENSE
├── README.md              # Project documentation
├── streamlit_app.py       # Main application file
├── streamlit_app_openai.py# Alternative OpenAI app
├── component.py           # Custom UI components and styles
├── requirements.txt       # Dependencies for the project
├── assets                 # Background images and sounds
│   ├── backgrounds        # UI-related assets
│   └── sounds             # Placeholder for future sound features
├── photos                 # Additional project photos
├── Database               # Resume data for embedding retrieval
│   └── Resume             # Resume images (pages 1 and 2)
└── Drawing.drawio         # Flowchart for app workflow

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🔍 Features

1. Interactive Resume Q&A

• Ask natural language questions about Muhammad Fahmi’s resume and get accurate, concise answers.

2. Resume Viewer

• Visualize both pages of the resume in a clean, side-by-side interface.

3. FAISS Database Integration

• Utilize efficient embedding-based retrieval for fast and relevant document search.

4. Gemini Generative Model

• Generate detailed answers based on retrieved resume context using Gemini 1.5 Flash.

5. Dynamic User Experience

• Responsive, visually appealing Streamlit interface.

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🔄 Project Workflow

1. 🌐 Environment Setup:

   • Install required libraries:

     pip install -r requirements.txt

   • Launch the app locally:

     streamlit run streamlit_app.py

2. 🔧 Database Preparation:

   • Generate embeddings using HuggingFace Models.
   • Load embeddings into a FAISS index for fast retrieval.

3. 🚀 Application Integration:

   • Set up Gemini API keys for generative responses.
   • Integrate retrieval and generation workflows into the Streamlit app.

4. 🎮 User Interaction:

   • Users can ask questions, view resumes, and receive answers seamlessly.

5. 🔄 Continuous Improvement:

   • Add new features, optimize embeddings, and improve app UX.

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🗺️ Code Workflow

The following Mermaid diagram illustrates the workflow of the code:

graph TD
    A[Start] --> B[Load Custom Styles]
    B --> C[Configure Gemini API]
    C --> D[Initialize Embedding Model]
    D --> E[Define Prompt Template]
    E --> F[Display Resume Images]
    F --> G[User Inputs Question]
    G --> H[Load FAISS Database]
    H -->|Success| I[Retrieve Relevant Documents]
    H -->|Error| J[Display Error Message]
    I --> K[Generate Answer with Gemini]
    K --> L[Display Answer]
    J --> M[End]
    L --> M

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🎉 Conclusion

The Advanced RAG Project is a practical demonstration of retrieval-augmented generation for real-world applications. With AI-powered Q&A based on personal resume data, this project bridges the gap between machine intelligence and user-centric design. It’s an ideal showcase for exploring advanced AI workflows and interactive app development.

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📚 References

• LangChain Documentation
• FAISS Documentation
• Streamlit Documentation
• Gemini API Documentation

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📜 License

Fahmi Zainal Custom License Unauthorized copying, distribution, or modification of this project is prohibited. For inquiries, contact the project owner.