...\n options=options,\n )\n"," File \"/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py\", line 368, in decode\n decoded = self.decode_complete(\n jwt,\n ......\n leeway=leeway,\n )\n"," File \"/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py\", line 265, in decode_complete\n decoded = self._jws.decode_complete(\n jwt,\n ......\n detached_payload=detached_payload,\n )\n"," File \"/usr/local/lib/python3.14/site-packages/jwt/api_jws.py\", line 270, in decode_complete\n self._verify_signature(\n ~~~~~~~~~~~~~~~~~~~~~~^\n signing_input,\n ^^^^^^^^^^^^^^\n ......\n options=merged_options,\n ^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n"," File \"/usr/local/lib/python3.14/site-packages/jwt/api_jws.py\", line 417, in _verify_signature\n raise InvalidSignatureError(\"Signature verification failed\")\n","jwt.exceptions.InvalidSignatureError: Signature verification failed\n"],"error_code":"JWTInvalidSignature"},"partial":false,"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NDI3NzU1NywianRpIjoiZDNiNzE5YWMtNWY3Yy00MmJhLWI3YTAtMmVlODI1YmViNzk5Iiwic3ViIjoiL2RhdGFzZXRzL2ZhaXRob2xvcGFkZS9haWFjdC1vcGVucmFnIiwiZXhwIjoxNzg0MjgxMTU3LCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.G9IB3kGgpHj-oFq89mMqMerAEemH2diBst966jRGgLFUWSz8Zqc-KtfXfREG9pZ_B9gZC5XHMblX6KNOQotJDQ"},"dataset":"faitholopade/aiact-openrag","isGated":false,"isPrivate":false,"hasParquetFormat":false,"isTracesDataset":false,"author":{"_id":"68c0102e4c4e9b23941f711b","avatarUrl":"/avatars/3c1177c9ff5b880e7b7cc87b38abda95.svg","fullname":"Faith Olopade","name":"faitholopade","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false},"compact":true,"isLoggedIn":false}">
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The JWT signature verification failed. Check the signing key and the algorithm.
Error code: JWTInvalidSignature Exception: InvalidSignatureError Message: Signature verification failed Traceback: Traceback (most recent call last): File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt decoded = jwt.decode( jwt=token, ...... options=options, ) File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode decoded = self.decode_complete( jwt, ...... leeway=leeway, ) File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete decoded = self._jws.decode_complete( jwt, ...... detached_payload=detached_payload, ) File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete self._verify_signature( ~~~~~~~~~~~~~~~~~~~~~~^ signing_input, ^^^^^^^^^^^^^^ ...... options=merged_options, ^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature raise InvalidSignatureError("Signature verification failed") jwt.exceptions.InvalidSignatureError: Signature verification failed
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EmbeddingContent","children":[],"isValid":true,"title":"Schema — table EmbeddingContent"},{"id":"decoding-the-embeddings","label":"Decoding the embeddings","children":[],"isValid":true,"title":"Decoding the embeddings"},{"id":"getting-started--semantic-search-in-15-lines","label":"Getting started — semantic search in ~15 lines","children":[],"isValid":true,"title":"Getting started — semantic search in ~15 lines"},{"id":"evaluation","label":"Evaluation","children":[],"isValid":true,"title":"Evaluation"},{"id":"known-limitations","label":"Known limitations","children":[],"isValid":true,"title":"Known limitations"},{"id":"attribution","label":"Attribution","children":[],"isValid":true,"title":"Attribution"},{"id":"citation","label":"Citation","children":[],"isValid":true,"title":"Citation"}],"classNames":"top-6"}">
EU AI Act — structural RAG corpus
A single-file, pre-embedded SQLite corpus of the EU AI Act — Regulation (EU) 2024/1689 — chunked on legal structure only: one chunk per article paragraph, per recital, per annex point, per Article 3 definition. Chapter, section, and article metadata live in columns, not smeared into the text. The artifact is the corpus, not a pipeline — a downloadable database file you query locally, not a hosted service and not an authoritative legal interpretation.
Release
Release v1.0.0 (prepared 2026-07-16)
Primary artifact aiact_openrag.db (SQLite, embeddings included)
Schema version 2 (recorded in the meta table)
Chunks 933
Source text EUR-Lex consolidated version, CELEX 02024R1689-20240712
Source commit f94a363
Label audit commit 03e7aff (docs/label-audit.md)
Licensing is mixed — no single licence applies indiscriminately to every file. The EU legal text is reused under Commission Decision 2011/833/EU and is not relicensed by this dataset; the original corpus structure, metadata, embeddings and documentation are CC BY 4.0; adapted benchmark data is CC BY 4.0; software associated with the source repository is Apache-2.0. See LICENSE for the path-level division (LICENSES.md is the same text under the repository's canonical filename) and NOTICE for the attribution notice.
This is not legal advice. This corpus is a retrieval artifact for research and engineering. The chunking, tier labels, and metadata are operator-derived with documented rules — they are not authoritative interpretations. In particular, risk_tier is a labelling convention derived from the Act's structure (see docs/derivation.md), not a legal determination. Consult the Official Journal text and qualified counsel for compliance decisions.
Contents
933 chunks: 180 recitals, 522 article-paragraph chunks, 68 Article 3 definitions, 163 annex points
BGE-M3 dense embeddings (1024-dim float32, L2-normalized) for every chunk
risk_tier / obligation_on labels derived only where the text is unambiguous — every rule cites its provision in docs/derivation.md; NULL rather than guess
ELI deep links to the exact provision on EUR-Lex
Staged application dates per Article 113
Source: EUR-Lex consolidated version 02024R1689-20240712 (English). The English consolidated text is character-identical to the OJ text (32024R1689); article numbering is identical in both, and both CELEX ids are recorded. The build verifies, mechanically and fatally: every article 1–113 appears exactly once; every text node of the normative text is consumed by exactly one chunk and survives verbatim into the output (zero silent drops); no empty chunks; Annex III point counts match an independent extraction.
Schema — table EmbeddingContent
column type notes
chunk_id INTEGER PRIMARY KEY
celex_id TEXT 02024R1689-20240712
content_type TEXT recital | article | annex | definition
chapter TEXT e.g. CHAPTER III — HIGH-RISK AI SYSTEMS
section TEXT e.g. SECTION 2 — Requirements for high-risk AI systems
article_num TEXT 6, 6(2), 3(34), recital 44, Annex III.4(a)
heading TEXT e.g. Article 6 — Classification rules for high-risk AI systems
heading_level INTEGER 1 recital, 3 article/annex intro, 4 paragraph/point, 5 sub-point
chunk_text TEXT the provision text, nothing else
risk_tier TEXT direct textual classification only: prohibited (Art 5(1)) | high (Arts 6(1)/6(2) + Annex III, rebuttable per Art 6(3)) | NULL. limited/minimal are reserved, never assigned — operator-derived, see disclaimer
regime_bucket TEXT regime association: prohibited-practices | high-risk | transparency | gpai | voluntary-codes | NULL
obligation_on TEXT chunk-level operative subject: provider | deployer | importer | distributor | NULL
eli_url TEXT deep link to the exact provision
date_in_force TEXT EARLIEST date the provision applies, per Art 113 staging (Annex I: NULL) — an application date, not the Regulation's entry-into-force date (1 August 2024)
embedding BLOB 1024 × float32, raw bytes, L2-normalized (BGE-M3)
continued INTEGER 0 normally; 1,2,… for parts of a paragraph split at ~1000 tokens
A meta table records the source URL, both CELEX ids, embedding model, source-text licence notice, schema_version = 2, and a *_semantics note for each of the four derived columns (risk_tier_semantics, regime_bucket_semantics, obligation_on_semantics, date_in_force_semantics) stating exactly what each column does and does not claim.
The four derived columns answer four different questions:
risk_tier — does this chunk's own operative text classify? Narrow and deliberately sparse: only direct textual classifications ("shall be prohibited", "shall be considered to be high-risk").
regime_bucket — which regulatory regime does this provision belong to? Structural association (chapter/annex membership and the Commission's conventional tier names), independent of whether the text classifies.
obligation_on — which enum role is the chunk's operative obligated subject? Judged per chunk from its own sentences; NULL for authority-, Commission-, or AI-Office-bound and descriptive chunks.
date_in_force — earliest application date per Article 113; not the entry-into-force date, and NULL where no single date is unambiguous.
Value counts (933 chunks):
column population
risk_tier prohibited 2 · high 35 · NULL 896
regime_bucket prohibited-practices 10 · high-risk 332 · transparency 7 · gpai 51 · voluntary-codes 4 · NULL 529
obligation_on provider 56 · deployer 17 · importer 7 · distributor 6 · NULL 847
Decoding the embeddings
import sqlite3, numpy as np
con = sqlite3.connect("aiact_openrag.db") con.row_factory = sqlite3.Row row = con.execute("SELECT * FROM EmbeddingContent WHERE article_num = '5(1)'").fetchone() vec = np.frombuffer(row["embedding"], dtype=np.float32) # shape (1024,)
Getting started — semantic search in ~15 lines
Encoding new queries needs the embedding model (pip install sentence-transformers); everything else is the standard library plus NumPy.
import sqlite3, numpy as np from sentence_transformers import SentenceTransformer
con = sqlite3.connect("aiact_openrag.db") rows = con.execute("SELECT chunk_id, article_num, heading, chunk_text, embedding " "FROM EmbeddingContent WHERE embedding IS NOT NULL").fetchall() mat = np.vstack([np.frombuffer(r[4], dtype=np.float32) for r in rows])
model = SentenceTransformer("BAAI/bge-m3") q = model.encode(["is emotion recognition at work prohibited?"], normalize_embeddings=True)[0] for i in np.argsort(-(mat @ q))[:5]: r = rows[i] print(f"{r[1]:<16} {r[2]}\n {r[3][:150]}…\n")
Filter by metadata instead of (or as well as) similarity — that is the point of structural chunking:
-- what the text itself classifies (direct, defensible per provision) SELECT article_num, chunk_text FROM EmbeddingContent WHERE risk_tier = 'prohibited';
-- everything belonging to a regime (the broader, structural grouping) SELECT article_num, heading FROM EmbeddingContent WHERE regime_bucket = 'high-risk' AND content_type = 'article';
Evaluation
The corpus was evaluated against the AI Act Evaluation Benchmark (Davvetas et al.) on retrieval, QA and risk-classification tasks; full numbers, methodology and provenance are in eval/RESULTS.md.
One finding deserves a plain-language caveat here. A three-family model panel (mistral-nemo, qwen2.5:14b, claude-fable-5) rating all 339 benchmark scenarios showed inter-model agreement collapsing exactly on the two tiers where classification F1 is lowest: per-tier Fleiss κ was 0.238 (limited) and 0.449 (minimal) versus 0.784 (prohibited) and 0.738 (high-risk). This is consistent with ambiguity or noise at the limited/minimal label boundary, and it means limited/minimal per-tier scores on this benchmark should not be read as pure retrieval-quality signals — but it is an exploratory finding with substantial caveats, not proof: the raters are themselves LLMs, so shared model difficulty on a genuinely hard boundary cannot be separated from label n
[truncated for AI cost control]