EU AI Act OpenRAG: 933 legally structured chunks and BGE-M3 embeddings in one SQLite file [P]

I have released EU AI Act OpenRAG, a downloadable corpus of Regulation (EU) 2024/1689 designed for RAG and legal-NLP experimentation.

Instead of sliding character windows, the corpus chunks on the Regulation’s legal structure:

• one chunk per article paragraph
• one per recital
• one per Article 3 definition
• one per annex point
• chapter, section and provision metadata stored separately

The resulting SQLite database contains 933 chunks and a normalized 1024-dimensional BGE-M3 embedding for every chunk.

It also includes exact EUR-Lex links, Article 113 application-date metadata and deliberately narrow derived labels. Direct textual classification is stored separately from broader regulatory-regime association, and ambiguous cases remain NULL.

I evaluated it against the AI Act Evaluation Benchmark using a like-for-like whole-unit baseline:

• scenario article recall@20: 0.541 structural vs 0.449 baseline
• QA article hit@10: 0.927 structural vs 0.898 baseline
• overall RAG classification remained close and was slightly lower on the structural corpus, suggesting that generator behaviour dominates that task more than chunk granularity

I have published the full results, limitations, derivation methodology, label audit and licensing breakdown rather than only the favourable metrics.

Dataset:

huggingface.co/datasets/faitholopade/aiact-openrag

I would appreciate technical feedback, particularly on the retrieval evaluation, structural chunking methodology and what additional baselines would be most useful.