"Audit-Grade Embedding of Structured Knowledge Artefacts"

Audit-Grade Embedding of Structured Knowledge Artefacts This study by Winfried Brückner / ruahAI presents a reproducible, data-free method for transforming structured YAML and JSON knowledge artefacts into 768-dimensional embeddings using the OpenAI text-embedding-3-small model.The resulting vector field represents a transparent interface between human conceptual reasoning and machine semantics, establishing a civil, Annex IV-compliant framework for AI governance and reproducible computational research. The publication includes the full methodology, anonymised index data, a redacted model card, and visual distributions of token and source composition.All vectors and artefacts remain proprietary under the ruahAI license catalogue; this release documents the scientific and ethical foundations of the embedding process.