3 repository-uri
Generating physical storage paths based on configurable patterns mapped to document metadata.
Distinct from Path Generation Templates: No candidates cover the generation of filesystem paths based on document metadata templates.
Explore 3 awesome GitHub repositories matching data & databases · Metadata-Driven Path Templating. Refine with filters or upvote what's useful.
Papra is a self-hosted document management system designed for digital archiving, organization, and retrieval. It serves as a centralized platform for storing files with a focus on security, providing an encrypted file archive using AES-256-GCM and a programmatic interface for managing documents and metadata via a REST API, SDK, and command line tools. The system distinguishes itself through an automated document ingestion engine that imports files via email forwarding, monitored folders, and webhook listeners. It further enhances discoverability by acting as an OCR document indexer, extracti
Creates dynamic filesystem storage paths by mapping document metadata to configurable naming patterns.
This project is a desktop application designed for archiving video content and animation series from the Bilibili platform to local storage. It functions as a media download manager that enables offline access to single-part clips and multi-part series by resolving remote video identifiers and manifests into downloadable file paths. The application distinguishes itself by supporting authenticated access, allowing users to inject stored session cookies to retrieve high-definition streams and premium content that would otherwise be restricted. It also incorporates download acceleration through
Parses remote video manifests to map complex media structures into individual downloadable file paths.
osxphotos is a command-line interface tool and programmatic database interface designed for managing and exporting media from Apple Photos libraries. It provides a bridge to the underlying system database, allowing for the automation of batch operations and the retrieval of technical metadata. The project distinguishes itself through a metadata-driven export system that uses custom templates to organize files into directory hierarchies. It can extract machine-learning data—including aesthetic scores and optical character recognition—and synchronize metadata across different libraries using un
Converts metadata into formatted strings for filenames or paths using a custom templating language.