Universal Media Orchestrator (UMO-Core)

Table of Contents

• Technical Overview
• System Architecture
• Core Capabilities
• Deployment Logic
• Security & Integrity
• Roadmap

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Technical Overview

UMO-Core is a high-performance media discovery and ingestion pipeline designed for autonomous operation. It leverages heuristic search algorithms and asynchronous task orchestration to manage large-scale media libraries.

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System Architecture

Component	Engineering Description	Technology Stack
Discovery Engine	Heuristic trend analysis via TMDB API endpoints.	Python / Requests / JSON
Ingestion Pipeline	Multi-threaded task queuing for concurrent media processing.	Concurrent.Futures / yt-dlp
Persistence Layer	Relational data mapping for persistent state tracking.	SQLite3 / Thread-Safe Locks
Remote Orchestrator	API-driven command & control via qBittorrent Web UI.	qbittorrent-api v2.0+
Control Interface	Glassmorphic management dashboard for real-time telemetry.	HTML5 / CSS3 / Flask-Jinja2

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Core Capabilities

[+] Autonomous Media Discovery (Discovery V2)

The system implements a polling mechanism that queries global trending endpoints. It applies content filters and genre-specific masks to prioritize high-value assets for internal ingestion.

[+] Asynchronous Torrent Routing

Incoming magnet links are validated and dispatched to the qBittorrent RPC interface. The orchestrator monitors the peer-to-peer state and updates the central database via event-driven hooks.

[+] Metadata Enrichment & Normalization

Automatically scrapes TMDB for poster assets and production metadata to ensure a normalized data structure across the library.

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Deployment Logic

Phase 1: Dependency Provisioning

The system requires the following binary environments:

• Python v3.10+: python.org/downloads
• qBittorrent (Web UI enabled): qbittorrent.org/download
• FFmpeg Engine: ffmpeg.org/download

Initialize the virtual environment and install the required dependency tree:

python -m venv venv
source venv/bin/activate  # atau .\venv\Scripts\activate pada Windows
pip install -r requirements.txt

Phase 2: Credential Acquisition & Injection

To interface with global media databases, you must provision the following tokens:

1. TMDB API Access (Discovery Layer)

• Endpoint: themoviedb.org/settings/api
• Protocol: Create a developer account and generate a v3 API Key to populate TMDB_API_KEY.

2. qBittorrent RPC (Ingestion Layer)

• Ensure Web UI is active in Tools -> Options -> Web UI.
• Map your local host and credentials to the .env container:

# Core API Access
TMDB_API_KEY=your_secured_token
# Torrent RPC Credentials
QB_URL=http://127.0.0.1:8080
QB_USER=admin
QB_PASS=adminadmin

Phase 3: Runtime Execution

Execute the master orchestrator to initiate the command-line interface:

python main.py

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Security & Integrity

The system is architected with a strict .gitignore policy to prevent sensitive credential leakage. The persistence layer utilizes thread-locking mechanisms to prevent race conditions during high-concurrency ingestion cycles.

[!] Disclaimer: This tool is designed for educational purposes and personal media management. Ensure compliance with local data regulations.

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Engineering Roadmap

The following modules are scheduled for upcoming development cycles:

• [ ] AI-Synced Subtitles: Deep learning models for automatic subtitle synchronization.
• [ ] Plex/Jellyfin Integration: Direct export of metadata to media server formats.
• [ ] Multi-Tenant Auth: Role-based access control (RBAC) for the Web Dashboard.
• [ ] P2P Health Monitoring: Advanced metrics for tracker and peer health.
• [ ] Distributed Ingestion: Multi-node support for high-throughput downloads.

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Build Status: STABLE | Deployment Model: EDGE | Architecture: MONOLITHIC-ORCHESTRATOR