anvilindex/anvil-index-rag-packs-sample

Anvil Index RAG Packs Sample Dataset

Overview

anvil-index-rag-packs-sample is a curated 20-chunk preview of Anvil Index RAG Packs. This dataset showcases the quality, richness, and versatility of Anvil Index knowledge bases, which deliver regulatory compliance data as semantic-first, vector-database-ready JSON artifacts.

RAG Packs are domain-specific, curated collections of regulatory documents chunked and enriched with controlled-vocabulary metadata. Each chunk links across regulatory domains, enabling deep compliance reasoning and cross-regulatory analysis.

Dataset Contents

This sample contains 20 representative chunks (4 per domain) across five specialized regulatory domains:

Governance Pack (4 chunks)

• EU AI Act Article 6: Classification rules for high-risk AI systems (EU regulation)
• NIST AI RMF 1.1: Risk management framework introduction (NIST guidance)
• NIST AI 600-1 GOVERN: Strategic governance for AI risk (NIST standards)
• OECD AI Due Diligence Step 1: Initial due diligence process (OECD framework)

Security Pack (4 chunks)

• OWASP LLM01:2025: Prompt injection vulnerability definition (threat modeling)
• NIST IR 8596 GOVERN: Secure AI development governance (NIST incident response)
• MITRE ATL&CK M0000: Reconnaissance tactics baseline (attack taxonomy)
• CISA Best Practices 001: Data security fundamentals (U.S. government guidance)

Privacy Pack (4 chunks)

• EU AI Act Article 10: Privacy requirements for AI systems (EU regulation)
• California SB-53 DEF: AI data definitions and scope (state regulation)
• EDPB-EDPS Joint Opinion: Algorithmic discrimination and transparency (EU guidance)
• CISA SBOM 2025-001: Software bill of materials for transparency (supply chain security)

Financial Services Pack (4 chunks)

• SR 11-7 Section 001: Model risk management introduction (Fed supervisory guidance)
• OCC MRM 001: Banking oversight of AI model risk (U.S. banking regulation)
• SEC EXAM 2026-001: Investment adviser examination procedures (SEC guidance)
• Treasury AI FinServ 001: AI governance for financial services (Treasury guidance)

Healthcare Pack (4 chunks)

• FDA SaMD 001: AI/ML in software as medical device strategy (FDA action plan)
• FDA MLT 001: Machine learning transparency and accountability (FDA guidance)
• HHS AI STR 001: U.S. healthcare AI strategy framework (HHS guidance)
• EMA AI RP 001: European regulatory perspective on AI in healthcare (EMA reflection)

Metadata Schema

Each chunk includes rich, structured metadata enabling advanced vector search, compliance filtering, and cross-regulatory reasoning. Key fields include:

Core Identifiers

• chunk_id: Unique identifier within its domain (e.g., "eu_ai_act_article_006")
• semantic_title: Human-readable title summarizing chunk content and regulatory context
• domain: Primary domain classification (AI_Governance, AI_Security_and_Threat_Modeling, Financial_Services_AI_Regulatory, Healthcare_AI_Medical_Devices, AI_Data_Privacy_Cross_Border_Compliance)

Source Authority

• source_document: Full official document title and identifier
• source_authority: Authoring entity (e.g., European Commission, FDA, Federal Reserve)
• jurisdiction: Geographic or institutional scope (European Union, US_Federal, California, etc.)
• source_url: Direct link to original official source
• document_type: Category (regulation, guidance, action plan, supervisory letter, etc.)

Content Structure

• section_reference: Location within source (e.g., Article 6, Chapter III, Section 1.1)
• section_type: Granular type (article, annex, step, principle, definition, etc.)
• chunk_type: Semantic category (prohibition, requirement, definition, best_practice, etc.)
• legal_domain: Regulatory area (e.g., AI governance, model risk management)

Compliance & Risk

• risk_tier: Risk classification (general, medium_risk, high_risk) where applicable
• compliance_obligation: Boolean flag indicating direct obligation to comply
• enforcement_dates: Timeline of regulatory deadlines (ISO 8601 format)
• penalties: Enforcement consequences if populated
• numerical_limits: Specific compliance thresholds where applicable

AI Actors & Roles

• ai_actors: Responsible parties under regulation (provider, deployer, regulator, auditor, etc.)
• affected_entity: Who the rule applies to (Device Manufacturer, Bank, AI Provider, etc.)
• regulatory_domain: Functional area (Device_Regulation, Model_Risk_Management, etc.)
• ai_lifecycle_stage: Points in deployment lifecycle where rule applies (Design, Development, Deployment, Monitoring)

Multi-Source Linkage

• cross_references: Links to related chunks across packs using canonical IDs (e.g., OWASP-LLM-TOP10-2025, NIST-AI-RMF-GOV-001)
• keywords: Controlled vocabulary terms for semantic search and clustering
• obligation_type: Category of requirement (Binding_Regulation, Regulatory_Guidance, Supervisory_Expectation, Best_Practice)

Curation

• pack_version: Version of the RAG Pack containing this chunk
• processing_date: When the chunk was ingested and enriched (ISO 8601)
• license: Intellectual property status where disclosed

File Structure

Each chunk in the sample consists of two files:

sample_chunks/
├── governance/
│   ├── eu_ai_act_article_006.md
│   ├── eu_ai_act_article_006_metadata.json
│   ├── nist_rmf_1_1.md
│   ├── nist_rmf_1_1_metadata.json
│   ├── nist_600_1_govern_1_1.md
│   ├── nist_600_1_govern_1_1_metadata.json
│   ├── oecd_ai_dd_step_1_1.md
│   └── oecd_ai_dd_step_1_1_metadata.json
├── security/
│   ├── owasp_llm01_definition.md
│   ├── owasp_llm01_definition_metadata.json
│   ├── ir8596_govern.md
│   ├── ir8596_govern_metadata.json
│   ├── atlas_aml_m0000.md
│   ├── atlas_aml_m0000_metadata.json
│   ├── cisa_best_practices_001.md
│   └── cisa_best_practices_001_metadata.json
├── privacy/
│   ├── EU-AIA-PRIV-ART10-001.md
│   ├── EU-AIA-PRIV-ART10-001_metadata.json
│   ├── CA-SB53-DEF-001.md
│   ├── CA-SB53-DEF-001_metadata.json
│   ├── EDPB-JO-2026-BG-001.md
│   ├── EDPB-JO-2026-BG-001_metadata.json
│   ├── CISA-SBOM-2025-001.md
│   └── CISA-SBOM-2025-001_metadata.json
├── finserv/
│   ├── SR-11-7-001.md
│   ├── SR-11-7-001_metadata.json
│   ├── OCC-MRM-001.md
│   ├── OCC-MRM-001_metadata.json
│   ├── SEC-EXAM-2026-001.md
│   ├── SEC-EXAM-2026-001_metadata.json
│   ├── TREAS-AI-FS-001.md
│   └── TREAS-AI-FS-001_metadata.json
└── healthcare/
    ├── FDA-SAMD-001.md
    ├── FDA-SAMD-001_metadata.json
    ├── FDA-MLT-001.md
    ├── FDA-MLT-001_metadata.json
    ├── HHS-AI-STR-001.md
    ├── HHS-AI-STR-001_metadata.json
    ├── EMA-AI-RP-001.md
    └── EMA-AI-RP-001_metadata.json

.md Files

Plain-text markdown content extracted from source documents. Suitable for keyword search, reading comprehension, and content-based filtering.

_metadata.json Files

Structured JSON containing all metadata fields listed above. Designed for consumption by vector databases, semantic search systems, and compliance filtering applications.

Key Qualities Demonstrated in This Sample

This sample was selected to highlight the most valuable features of Anvil Index RAG Packs:

1. Authoritative sources: All chunks come directly from official regulatory documents published by recognized authorities (EU, FDA, NIST, Federal Reserve, etc.)

2. Rich cross-referencing: Metadata includes links to related chunks across domains, enabling queries like "What FDA guidance applies to the EU AI Act's high-risk requirements?" using canonical chunk IDs.

3. Controlled vocabularies: Fields like ai_actors, regulatory_domain, ai_lifecycle_stage, and obligation_type use consistent enumerations across all packs, enabling reliable filtering and faceted search.

4. Compliance-ready structure: Boolean compliance_obligation, enforcement dates, penalties, and role classifications directly support compliance workflows and regulatory reporting.

5. Semantic precision: Chunk titles, types, and keywords reflect the conceptual intent of source passages, not just surface-level keywords. This enables semantic search and ensures results are contextually relevant.

6. Domain specialization: Chunks vary meaningfully by domain (governance, security, privacy, financial services, healthcare), demonstrating how Anvil Index covers diverse regulatory landscapes with specialized vocabularies.

7. Temporal rigor: Enforcement dates and processing timestamps support time-aware compliance calendars and version-sensitive regulatory tracking.

Use Cases

These chunks support:

• RAG system training: Evaluate chunk quality, schema, and cross-referencing before committing to a full pack.
• Vector database prototyping: Test embedding, retrieval, and ranking pipelines against real regulatory content.
• Compliance workflow design: Build applications that filter by ai_actors, risk_tier, enforcement_dates, and cross-references.
• Regulatory research: Explore how different jurisdictions (EU, US, California, healthcare, financial) address the same AI governance topics.
• Multi-modal AI analysis: Understand how RAG chunks combine semantic titles, structured metadata, and source content for comprehensive LLM context windows.

Getting Started

1. Download the dataset from Hugging Face.
2. Explore a sample chunk: Read one .md file and its corresponding _metadata.json to understand the structure.
3. Test your vector DB: Load the 20 chunks into your embedding model and vector database to evaluate performance.
4. Check cross-references: Use the cross_references field to follow regulatory connections across domains.
5. Scale to full packs: When ready, purchase the complete RAG Packs for your domain of interest.

Licensing

This sample dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share, adapt, and use this dataset for any purpose, provided you give appropriate credit to Anvil Index.

Underlying source documents retain their original licenses where applicable. OWASP content is CC BY-SA 4.0. All other material is governed by fair use and public domain licensing.

Full Product Information

This 20-chunk sample is a free lead magnet showcasing the quality and depth of Anvil Index RAG Packs.

Full RAG Packs Available:

• AI Governance Pack v1.2.1: 339 chunks from 8 documents
• AI Security Pack v1.2.0: 190 chunks from 5 documents
• AI Privacy Pack v1.0.1: 111 chunks from 6 documents
• Financial Services Pack v2.0.0: 208 chunks from 12 documents
• Healthcare Pack v2.0.0: 190 chunks from 13 documents

Pricing:

• Single pack: $29
• Complete Forge Bundle (all 5 packs): $99

Purchase:

• https://anvilindex.com

Why Choose Anvil Index RAG Packs:

1. Curated authoritative sources: Every chunk comes from official regulatory documents vetted by legal and AI experts. No hallucinated, paraphrased, or secondary-source content.

2. Production-ready metadata: Rich, structured fields enable sophisticated filtering, ranking, and compliance workflows without post-processing.

3. Cross-regulatory intelligence: Canonical cross-references span all five packs, enabling multi-domain compliance queries (e.g., "Which FDA requirements align with NIST RMF GOVERN?").

4. Semantic precision: Chunks are conceptually coherent and sized for LLM context windows, not arbitrary word counts. Metadata titles reflect meaning, not keywords.

5. Regular updates: Packs are versioned and updated as regulations evolve. Subscription models available for long-term compliance tracking.

6. Licensed for commercial use: CC BY 4.0 and fair-use compliance allow integration into commercial RAG systems, compliance platforms, and legal tech products.

Support

For questions about this sample dataset, licensing, or the full product line, contact: support@anvilindex.com

For issues specific to this Hugging Face dataset, open a discussion on the dataset page.

Citation

If you use this dataset in academic or commercial work, please cite as:

@dataset{anvil_index_rag_packs_sample,
  title = {Anvil Index RAG Packs Sample Dataset},
  author = {Anvil Index},
  year = {2026},
  month = {March},
  url = {https://huggingface.co/datasets/anvilindex/rag-packs-sample},
  license = {CC-BY-4.0},
  note = {20 representative chunks across 5 regulatory domains: AI Governance, AI Security, AI Privacy, Financial Services, Healthcare}
}

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Last updated: March 29, 2026 Dataset version: 1.0.0 Total chunks: 20 (4 per domain, 40 files including .md and .json)