"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.结构化知识载体的审计级嵌入 本研究由温弗里德·布吕克纳（Winfried Brückner）与ruahAI团队完成，提出了一种可复现、无数据依赖的方法：借助OpenAI的text-embedding-3-small模型，可将结构化YAML与JSON格式的知识载体转换为768维嵌入向量。所生成的向量空间可作为人类概念推理与机器语义之间的透明交互接口，同时构建了符合Annex IV要求的温和型人工智能治理与可复现计算研究框架。 本次公开资料涵盖完整的研究方法论、匿名化索引数据集、脱敏版模型卡片，以及Token与源数据构成的可视化分布结果。所有向量与知识载体均受ruahAI许可目录下的专有协议保护；本发布仅旨在阐明嵌入流程的科学与伦理基础。