extracta 0.2.2

Modular content analysis platform for research, assessment, and academic integrity checking

Extracta

Modular Content Analysis Platform for research, assessment, and academic integrity checking.

Extracta provides a unified interface for extracting and analyzing content from diverse media types including documents, images, repositories, and web content. It supports both research-focused deep analysis and assessment-oriented quality evaluation, with specialized tools for academic integrity validation.

✨ Key Features

• 🧩 Modular Architecture: Pluggable lenses and analyzers for different content types
• 📚 Academic Integrity: Citation-reference validation, bibliography checking, URL verification, AI conversation analysis
• 🤖 AI Conversation Analysis: Cognitive intent classification for AI-assisted learning assessment
• 🔍 Multiple Analysis Modes: Research and assessment workflows
• 📄 Rich Content Support: Text, images, documents, repositories, presentations, spreadsheets, AI conversations
• 🎯 Rubric-Based Assessment: Custom rubrics for structured evaluation
• 🛡️ Security First: Input sanitization, URL validation, malicious content detection
• 🧠 Intelligent Analysis: Pattern detection, quality scoring, integrity validation, learning pattern recognition
• 💻 Multiple Interfaces: CLI, Python API, and Web API
• 🔧 Modern Python: Built with uv, ruff, mypy, and pytest

Installation

From PyPI

pip install extracta

From Source

git clone https://github.com/michaelborck-education/extracta.git
cd extracta
pip install -e .

Optional Dependencies

Install with specific feature support:

pip install extracta[audio]     # Audio processing (faster-whisper for Apple Silicon)
pip install extracta[video]     # Video processing
pip install extracta[text]      # Enhanced text analysis (spaCy, NLTK)
pip install extracta[image]     # Image analysis with OCR
pip install extracta[code]      # Code analysis
pip install extracta[citation]  # Academic integrity (CrossRef, URL validation)
pip install extracta[conversation]  # AI conversation analysis (Gemini default)
pip install extracta[openai]    # OpenAI LLM provider
pip install extracta[claude]    # Anthropic Claude LLM provider
pip install extracta[openrouter] # OpenRouter unified API
pip install extracta[api]       # Web API server (FastAPI, Uvicorn)
pip install extracta[all]       # All features

Usage

Command Line

Basic Content Analysis

# Analyze document for research insights
extracta analyze research_paper.pdf --mode research --output analysis.json

# Assess student submission quality
extracta analyze essay.docx --mode assessment --output feedback.json

# Analyze repository structure and content
extracta analyze https://github.com/user/repo --mode assessment

Academic Integrity Checking

# Comprehensive citation and reference validation
extracta citation analyze student_paper.pdf --output integrity_check.json

# AI conversation cognitive intent analysis (with different LLM providers)
extracta citation conversation chatgpt_export.json --provider gemini --output analysis.json
extracta citation conversation chat.json --provider claude --model claude-3-sonnet-20240229
extracta citation conversation chat.json --provider openai --model gpt-4
extracta citation conversation chat.json --provider openrouter --model anthropic/claude-3-haiku

# Results include:
# - Citation-reference relationship validation
# - Bibliography padding detection
# - URL accessibility and domain reputation
# - AI conversation learning pattern analysis
# - Academic integrity scoring

Python API

Basic Content Analysis

from extracta import TextAnalyzer

analyzer = TextAnalyzer()
result = analyzer.analyze(text_content, mode="research")
print(result)

Academic Integrity Analysis

from extracta.analyzers import CitationAnalyzer, ReferenceAnalyzer, URLAnalyzer, ConversationAnalyzer

# Citation-reference validation
citation_analyzer = CitationAnalyzer()
citation_result = citation_analyzer.analyze(document_text)

# Bibliography quality assessment
reference_analyzer = ReferenceAnalyzer()
reference_result = reference_analyzer.analyze(document_text)

# URL validation and reputation checking
url_analyzer = URLAnalyzer()
url_result = url_analyzer.analyze(document_text)

# AI conversation cognitive intent analysis (with different providers)
conversation_analyzer = ConversationAnalyzer(provider="claude", model="claude-3-sonnet-20240229")
conversation_result = conversation_analyzer.analyze(conversation_json_data)

# Or use OpenAI
conversation_analyzer = ConversationAnalyzer(provider="openai", model="gpt-4")
conversation_result = conversation_analyzer.analyze(conversation_json_data)

# Combined integrity score
integrity_score = citation_result['citation_analysis']['academic_integrity_score']
learning_quality = conversation_result['conversation_analysis']['learning_assessment']['learning_quality_score']
print(f"Academic Integrity Score: {integrity_score}/100")
print(f"AI Learning Quality Score: {learning_quality}/100")

Grading and Assessment

from extracta.grading.rubric_manager import RubricRepository, get_default_rubric
from extracta.grading.feedback_generator import FeedbackGenerator

# Load or create a rubric
repo = RubricRepository("rubrics")
rubric = get_default_rubric("academic")  # or repo.load("my-rubric")

# Generate feedback based on analysis results
generator = FeedbackGenerator()
feedback = generator.generate_feedback(
    rubric=rubric,
    analysis_data=analysis_result,
    audience="student",
    detail="detailed"
)

🎓 Academic Integrity Features

Extracta provides comprehensive tools for detecting academic integrity issues and validating scholarly work:

Citation Analysis

• Citation-Reference Validation: Ensures all references have corresponding in-text citations
• Bibliography Padding Detection: Identifies references without citations
• Citation Stuffing Detection: Flags excessive citations in single sentences
• Style Recognition: Supports APA, MLA, Chicago, Harvard, and Numeric styles

Reference Validation

• DOI Verification: Validates Digital Object Identifiers with CrossRef API
• URL Accessibility: Checks if referenced URLs are accessible (404 detection)
• Domain Reputation: Analyzes source credibility (academic vs. commercial domains)
• Format Validation: Ensures proper reference formatting and completeness

AI Conversation Analysis

• Cognitive Intent Classification: Uses configurable LLM to classify user prompts as Delegation vs. Scaffolding
• Multi-Provider Support: Gemini, OpenAI GPT, Anthropic Claude, OpenRouter unified API
• Learning Pattern Recognition: Analyzes conversation flow for active learning behaviors
• Session Quality Scoring: Provides learning quality assessment (0-100)
• Platform Support: ChatGPT, Claude, Bard, and generic conversation formats

Security & Privacy

• Input Sanitization: Detects and prevents malicious content, hidden text, and LLM jailbreaks
• URL Protection: SSRF prevention with academic domain whitelisting
• Content Validation: Size limits, encoding validation, and integrity checking
• Privacy First: No data persistence, user-controlled processing, ephemeral analysis
• Safe Processing: Static analysis only, no code execution or external script running

Repository Analysis

• WordPress Detection: Identifies WordPress projects and analyzes themes/plugins
• Code Quality Assessment: Evaluates repository structure and practices
• File Type Analysis: Comprehensive analysis of all repository contents

Integrity Scoring

• Academic Integrity Score: 0-100 scale based on multiple validation criteria
• Detailed Reporting: Specific issues and recommendations
• Pattern Detection: Identifies suspicious citation and reference patterns

Development

Setup

# Clone repository
git clone https://github.com/michaelborck-education/extracta.git
cd extracta

# Create virtual environment
uv venv
source .venv/bin/activate

# Install dependencies
uv pip install -e ".[dev]"

Testing

# Run tests
pytest

# With coverage
pytest --cov=extracta

Linting and Type Checking

# Lint with ruff
ruff check .

# Type check with mypy
mypy extracta

# Format code
ruff format .

Building and Publishing

# Build package
uv build

# Publish to PyPI
uv venv  # if not already
source .venv/bin/activate
uv pip install twine
twine upload dist/* --repository pypi

Project Structure

extracta/
├── extracta/
│   ├── lenses/              # Content extraction modules
│   │   ├── audio_lens/      # Audio file processing
│   │   ├── video_lens/      # Video file processing
│   │   ├── image_lens/      # Image processing with OCR
│   │   ├── document_lens/   # Text & Office document processing
│   │   ├── presentation_lens/ # Presentation file analysis
│   │   ├── repo_lens/       # Repository-level analysis
│   │   └── base_lens.py     # Common lens interface
│   ├── analyzers/           # Content analysis modules
│   │   ├── text_analyzer/   # Text quality and readability
│   │   ├── image_analyzer/  # Image quality assessment
│   │   ├── citation_analyzer/ # Citation-reference validation
│   │   ├── reference_analyzer/ # Bibliography quality assessment
│   │   ├── url_analyzer/    # URL validation and reputation
│   │   └── base_analyzer.py # Common analyzer interface
│   ├── grading/             # Assessment and grading
│   │   ├── rubric_manager/  # Rubric creation and management
│   │   └── feedback_generator.py # AI-powered feedback
│   ├── orchestration/       # Workflow management
│   ├── shared/              # Common utilities
│   └── cli/                 # Command-line interface
├── tests/                   # Test suite
├── docs/                    # Documentation
├── examples/                # Usage examples
├── pyproject.toml           # Package configuration
└── README.md               # This file

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Run the test suite
6. Submit a pull request

License

MIT License - see LICENSE file for details.

🚀 Current Status & Roadmap

✅ Implemented Features

• Text Analysis: Readability, sentiment, vocabulary, quality metrics
• Image Analysis: OCR, quality assessment, accessibility
• Document Processing: PDF, DOCX, Office docs (PPTX, Excel, CSV)
• Citation Validation: Citation-reference relationships, academic integrity
• Reference Analysis: Bibliography quality, DOI validation, CrossRef integration
• URL Validation: Accessibility checking, domain reputation, robots.txt
• AI Conversation Analysis: Cognitive intent classification, learning pattern recognition
• Repository Analysis: GitHub repo analysis, WordPress detection
• Rubric System: Custom rubrics, structured assessment
• CLI Interface: Multiple commands for different analysis types
• Web API: REST API for integration
• Python API: Programmatic access

🔄 In Development

• Audio Lens: Speech-to-text, audio quality analysis
• Video Lens: Frame analysis, transcript processing
• Code Analyzer: Code quality metrics, best practices
• Screenshot Integration: Visual URL validation
• Wayback Machine: Archive URL checking

📋 Future Enhancements

• URL Conversation Input: Direct analysis of conversations from URLs (ChatGPT share links, etc.)
• GUI Application: Web-based interface
• LMS Integration: Canvas, Blackboard, Moodle
• Advanced ML Models: Fine-tuned for educational content
• Collaborative Features: Multi-user assessment workflows
• Plugin Architecture: Custom lenses and analyzers