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MetaDiscovery Agent – Design Summary and Purpose
MetaDiscovery Agent is an AI-enhanced metadata analysis and augmentation tool built with Streamlit, designed for use with digital collections from the Library of Congress (LOC). By integrating LOC's JSON API, the agent enables users—researchers, librarians, and digital humanities professionals—to audit, visualize, and enhance metadata completeness with ease.

Development Summary
Framework: Built using Streamlit with responsive UI styling, custom CSS themes, and a clean white aesthetic tailored for professional research environments.

API Integration:

  • Pulls real-time data from the Library of Congress Search API.

  • Uses a secure, user-defined collection selector for focused retrieval.

  • Handles record parsing, structure normalization, and error feedback dynamically.

Metadata Analysis System:

  • Automatically computes field completeness across common metadata categories (title, date, subject, creator, description).

  • Displays completeness metrics via interactive bar charts using Plotly.

  • Identifies records missing essential metadata and flags them for enhancement.

Enhancement & Intelligence Layer:

  • Uses TF-IDF and cosine similarity to propose subject metadata for records lacking descriptors.

  • Provides unique metadata suggestions based on comparison with semantically similar descriptions.

  • Filters out low-confidence matches to maintain human oversight and editorial integrity.

UI & UX Features:

  • Local banner image for institutional branding or customization.

  • Fully styled light theme with readable dark gray text and visually consistent alert blocks.

  • Responsive layout that works across devices, ensuring accessibility in lab, classroom, or field settings.

Context-Aware Design:

  • Dynamic dropdown selector for seamless switching across LOC collections.

  • Built-in checks for non-informative metadata (e.g., “null”, empty strings).

  • Expandable architecture ready for future features like export, tagging, or authority linking.

Purpose
MetaDiscovery Agent is built to:

  • Help librarians, researchers, and digital curators rapidly evaluate metadata quality across LOC collections.

  • Prioritize high-value records with incomplete metadata for enrichment.

  • Suggest metadata enhancements using natural language processing and content comparison.

  • Support digital stewardship and access equity by improving discoverability across diverse collections.

By blending AI-powered metadata analysis with human-centric review workflows, MetaDiscovery Agent aims to modernize digital curation and empower collaborative metadata enhancement in cultural heritage environments.