README.md

metadata

title: SecureVault AI
emoji: 🔐
colorFrom: blue
colorTo: gray
sdk: streamlit
sdk_version: 1.42.0
app_file: main.py
pinned: false

SecureVault AI: Privacy-Focused RAG System

A secure note-taking application implementing Retrieval-Augmented Generation (RAG) and Zero-Knowledge Encryption. This project demonstrates the integration of LLMs with local vector databases while maintaining high data privacy standards.

Live Demo: SecureVault AI on Hugging Face

Core Implementation Features

• Security Architecture: Implements a hybrid model using PBKDF2 for key derivation and AES-256 (Fernet) for symmetric encryption. All decryption happens strictly in-memory during session runtime.
• Vector Search: Uses FAISS and the all-MiniLM-L6-v2 transformer model for semantic indexing. This enables local retrieval without exposing sensitive data to external APIs.
• Resource Monitoring: Built-in tracking for token consumption and API costs, paired with a feedback mechanism for monitoring RAG retrieval accuracy.
• Session Management: Automated "Zero-Knowledge" protection. If the Master PIN is lost, encrypted data is mathematically unrecoverable.
• Automated Security: Includes a session-based auto-lock engine that purges decrypted data from memory after 150 seconds of inactivity.
• Fail-Safe Recovery: Implements a hashed Recovery Key system for account resets while maintaining the mathematical integrity of encrypted data.
• Smart Exports: Unicode-compliant PDF and DOCX generation with specialized font embedding for multi-language support.

Engineering Challenges & Production Fixes

Deploying from a local Windows environment to Hugging Face (Debian Linux) required solving several infrastructure-level issues:

• Character Encoding Fixes: Resolved UnicodeEncodeError in the PDF export module by implementing a preprocessing pipeline to handle non-Latin characters in a Linux environment.
• Binary Serialization: Fixed RuntimeError issues with Streamlit's download triggers by explicitly casting file buffers to bytes objects to ensure consistent behavior across OS environments.
• Version Compatibility: Refactored the UI components to maintain stability across Streamlit versions (specifically addressing the removal of deprecated icon arguments in cloud deployments).

Technical Stack

• UI/UX: Streamlit (Session State & Custom CSS)
• AI/LLM: Google Gemini API, Sentence-Transformers, FAISS
• Security: Python cryptography library, SHA-256 hashing
• Persistence: JSON-based local storage

Installation

1. Clone the repository:

   git clone [https://github.com/mubi0613/SecureVault-AI.git](https://github.com/mubi0613/SecureVault-AI.git)
   cd SecureVault-AI
   

2. Setup environment: Ensure you have Python 3.9+ installed, then run:

   pip install -r requirements.txt
   

3. Run locally:

   streamlit run main.py
   

Project Structure

SecureVault-AI/

• ├── main.py # Streamlit UI & Session Management
• ├── vault_logic.py # Cryptography, RAG, & File IO Logic
• ├── requirements.txt # Project Dependencies
• ├── .github/workflows/ # Auto-sync to Hugging Face
• ├── fonts/ # Custom fonts for cross-platform PDF rendering
• └── README.md # Documentation

Architecture Diagram

graph TD
    subgraph "User Interface (Streamlit)"
        A[User Input] --> B{Vault Status}
    end

    subgraph "Security Layer"
        B -- Unlocked --> C[AES-256 Decryption]
        B -- Locked --> D[Access Denied]
    end

    subgraph "RAG Engine"
        C --> E[FAISS Vector Index]
        E --> F[Context Retrieval]
        F --> G[Gemini Pro LLM]
    end

    G --> H[Final Secure Answer]

Important Note on Security

This tool follows Zero-Knowledge principles. Neither the developer nor the host can recover data if the Master PIN and Recovery Key are lost.

Usage Guide

• Initialize: Set your Master PIN and save your Recovery Key.
• Create Notes: Use the sidebar to add notes. Toggle "Mark as Secret" to apply AES-256 encryption.
• AI Search: Open the "Ask Your Vault" expander to query your notes using natural language.
• Manage: Edit or delete notes, and export them as PDF/DOCX for offline use.
• Monitor: Check the "AI Resources" section in the sidebar to view estimated token usage and costs.