📂 File Management and Visualization System

A powerful File Management and Visualization System that allows users to register, login, upload/download files, visualize data, and leverage AI-powered analysis such as embedding search, anomaly detection, and recommendations.

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🖼️ System Overview

Figure: Architecture of the File Management and Visualization System.

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🛠️ Key Components

1. UI Layer (Frontend)

• Technologies: React.js (or Next.js)
• Features:
  • 🔐 Login/Registration: User authentication with JWT or OAuth (Google).
  • 📊 Dashboard: Display file analytics, charts, and visualizations.
  • 📤 Upload/Download Files: Interface for file management.
  • 📈 Visualization: Use charts for file analytics via OpenSearch Dashboards or other tools.
  • 🔍 Search: Search indexed files by metadata or embeddings.

2. Node.js API (Backend 1)

• Technologies: Node.js (Express.js), MongoDB
• Responsibilities:
  • 👤 User Authentication: Handle login, registration, and JWT generation.
  • 🗂️ File Metadata Storage: Store and manage metadata (e.g., file name, type, size, permissions) in MongoDB.
  • 🗃️ File Handling API: Handle file uploads/downloads using cloud storage (e.g., AWS S3, GCP Cloud Storage).
  • 🔄 Search Requests: Pass search requests (metadata or embedding-based) to OpenSearch.

3. Flask API (Backend 2)

• Technologies: Python, TensorFlow, Transformers, Keras, Numpy
• Responsibilities:
  • 📁 Feature Extraction: Extract embeddings from uploaded files using AI models.
  • 🤖 Machine Learning Tasks: Process data for embedding generation, anomaly detection, and AI-driven recommendations.
  • 🔗 Communication: Interface with Node.js API for tasks requiring machine learning.

4. Storage Layer

• Technologies: Cloud-based storage (AWS S3, Google Cloud Storage, Azure Blob Storage)
• Responsibilities:
  • 💾 File Storage: Securely store all uploaded files.
  • 📦 Versioning & Backup: Ensure version control and data integrity.
  • 🔑 Access Control: Manage access via signed URLs based on user permissions.

5. OpenSearch (Search and Indexing)

• Technologies: OpenSearch
• Responsibilities:
  • 🔍 Indexing: Index files for fast retrieval using metadata and embeddings.
  • 🧠 Embedding Search: Perform vector-based searches for similar content.
  • 📊 Analytics: Perform aggregations and custom queries for data insights.
  • 🖥️ Visualization: Use OpenSearch Dashboards for real-time data visualizations.

6. OpenSearch Dashboards (Visualization and AI Assistance)

• Technologies: OpenSearch Dashboards (Kibana), Machine Learning Modules
• Responsibilities:
  • 📊 Visualization: Provide real-time visualizations of queries, file statistics, and analytics.
  • 🔍 Anomaly Detection: Detect unusual patterns in file usage or content.
  • 🤖 Recommendation Engine: Use AI-driven models to suggest relevant content to users.
  • 💬 Chat Assistant: Integrate a chat-based interface for answering queries or interacting with data.

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⚙️ System Workflow

1. User Interaction (UI Layer)

   • Users register or log in via the UI Layer.
   • The dashboard provides file analytics, visualizations, and search functionalities.

2. File Upload and Processing

   • The Node.js API handles file metadata storage and actual file storage in Cloud Storage.
   • The Flask API generates embeddings by processing the file content.

3. Indexing in OpenSearch

   • The Flask API sends embeddings to the Node.js API, which forwards them to OpenSearch for indexing.

4. Search and Visualization

   • Users can search files by metadata or embeddings.
   • OpenSearch Dashboards visualizes search results and file analytics.

5. Anomaly Detection and Recommendations

   • Based on file patterns, the system suggests files or detects anomalies using AI.

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📚 Technology Stack

1. Frontend (UI Layer)

• React.js or Next.js for the user interface.
• JWT/OAuth for authentication.
• Chart.js or D3.js for visualizations.

2. Backend 1 (Node.js API)

• Node.js (Express.js) for file metadata handling, uploads/downloads, authentication, and search queries.
• MongoDB for metadata storage.
• AWS S3 or GCP for file storage.

3. Backend 2 (Flask API)

• Flask for machine learning tasks.
• TensorFlow, Transformers, Keras for embeddings, anomaly detection, and recommendations.

4. Search and Analytics

• OpenSearch for indexing and search.
• OpenSearch Dashboards (Kibana) for visualizations and real-time analytics.

5. File Storage

• AWS S3, Google Cloud Storage, or Azure Blob Storage for secure file storage with signed URLs.

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🔧 Additional Considerations

• Scalability: Ensure Flask API and OpenSearch scale horizontally to handle data and query loads.
• Security: Implement strong authentication and authorization controls.
• Performance: Cache frequently accessed files and batch process embedding generation.
• Monitoring & Logging: Use OpenSearch Dashboards for system monitoring and performance tracking.

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📈 Future Enhancements

• Cloud GPU Support: Adding cloud-based GPUs for faster embeddings and AI processing.
• Multi-Language Support: Add multi-language processing for international users.
• Collaboration Features: Enable real-time file sharing and team collaboration.