Annex IV Review (annex4ac)
Анализ PDF документов на соответствие требованиям EU AI Act Annex IV и GDPR.
⚠️ Legal Disclaimer: This software is provided for informational and compliance assistance purposes only. It is not legal advice and should not be relied upon as such. Users are responsible for ensuring their documentation meets all applicable legal requirements and should consult with qualified legal professionals for compliance matters. The authors disclaim any liability for damages arising from the use of this software.
🔒 Data Protection: All processing occurs locally on your machine. No data leaves your system.
🚀 Quick‑start
# 1 Install (Python 3.9)
pip install annex4ac
# 2 Review single PDF document
from annex4ac import review_single_document
from pathlib import Path
issues = review_single_document(Path("technical_documentation.pdf"))
for issue in issues:
print(f"{issue['type']}: {issue['message']}")
# 3 Review multiple PDF documents
from annex4ac import review_documents
issues = review_documents([
Path("doc1.pdf"),
Path("doc2.pdf")
])
# 4 Analyze text content directly
from annex4ac import analyze_text
issues = analyze_text("AI system content...", "document.txt")
✨ Features
Advanced NLP Analysis
- Intelligent negation detection: Uses spaCy and negspaCy for accurate analysis
- Contradiction detection: Finds inconsistencies within and between documents
- Section validation: Checks all 9 required Annex IV sections
- GDPR compliance: Analyzes data protection and privacy issues
Compliance Checks
- Missing sections: Identifies absent Annex IV sections (1-9)
- High-risk classification: Detects high-risk use cases without proper labeling
- Data protection: Checks GDPR compliance requirements
- Transparency: Verifies explainability and bias detection mentions
Multiple Input Formats
- PDF files: Supports PyPDF2, pdfplumber, and PyMuPDF
- Text content: Direct text analysis
- Batch processing: Review multiple documents simultaneously
📋 API Reference
Core Functions
review_documents(pdf_files: List[Path], batch_size: int = 128) -> List[dict]
Review multiple PDF documents for compliance issues.
Parameters:
pdf_files: List of Path objects pointing to PDF filesbatch_size: Number of pages to process in each batch (default: 128)
Returns:
List of structured issue dictionaries with keys: type, section, file, message
review_single_document(pdf_file: Path) -> List[dict]
Review a single PDF document for compliance issues.
analyze_text(text: str, filename: str = "document") -> List[dict]
Analyze text content for compliance issues.
extract_text_from_pdf(pdf_path: Path) -> str
Extract text from PDF file using available libraries.
HTTP API Support
handle_multipart_review_request(headers: dict, body: bytes) -> dict
Handle multipart/form-data request for document review.
handle_text_review_request(text_content: str, filename: str = "document.txt") -> dict
Handle text review request.
create_review_response(issues: List[dict], processed_files: List[str]) -> dict
Create structured response for review results.
🔍 Issue Types
Errors (Critical Issues)
- Missing required Annex IV sections
- Contradictions between documents
- High-risk use cases without proper classification
- GDPR violations (indefinite data retention, missing legal basis)
Warnings (Recommendations)
- Missing transparency or explainability mentions
- No bias detection or fairness measures
- Missing security or robustness measures
- Only negative mentions of compliance terms
📊 Example Output
============================================================
COMPLIANCE REVIEW RESULTS
============================================================
❌ ERRORS (2):
1. [doc1.pdf] (Section 1) Missing content for Annex IV section 1 (system overview).
2. [doc2.pdf] (Section 5) No mention of risk management procedures.
⚠️ WARNINGS (1):
1. [doc1.pdf] No mention of transparency or explainability.
Found 3 total issue(s): 2 errors, 1 warnings
🛠 Requirements
- Python 3.9
- PDF Processing: PyPDF2, pdfplumber, or PyMuPDF
- NLP Analysis: spaCy, negspaCy, nltk
📚 References
- Annex IV HTML – https://artificialintelligenceact.eu/annex/4/
- EU AI Act – https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689
📄 Licensing
This project is licensed under the MIT License - see the LICENSE file for details.
The software assists in preparing documentation, but does not confirm compliance with legal requirements or standards. The user is responsible for the final accuracy and compliance of the documents.
Metadata
Release files for annex4review 1.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| annex4review-1.1.3.tar.gz | 16.4 kB | Without provenance | Details for annex4review-1.1.3.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| annex4review-1.1.3-py3-none-any.whl | Python 3 | none | any | Details for annex4review-1.1.3-py3-none-any.whl |
Total release size: 31.1 kB
Release files / annex4review-1.1.3.tar.gz
| Download URL | annex4review-1.1.3.tar.gz |
|---|---|
| Size | 16.4 kB |
| Tags | Source |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.6
|
Release files / annex4review-1.1.3-py3-none-any.whl
| Download URL | annex4review-1.1.3-py3-none-any.whl |
|---|---|
| Size | 14.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.6
|
Security for annex4review 1.1.3
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