inceptbench 1.5.0

Comprehensive benchmark and evaluation framework for educational AI question generation

InceptBench

Educational content evaluation framework with multiple AI-powered assessment modules.

📖 Documentation

Official Sites

Website • Benchmarks • Glossary • Docs • API Endpoint • API Docs

User Guides

• USAGE.md - Installation, configuration, CLI & Python API
• INPUT_OUTPUT.md - Input schemas and output formats
• EVALUATORS.md - Complete evaluator reference

Developer Guides

• WIKI.md - Documentation hub and workflows
• MAINTAINERS.md - Submodule maintainer guide
• PUBLISHING.md - Package publishing workflow
• VERSION_LOCATIONS.md - Version file reference

Resources

• Google Drive - Test data and examples
• GitHub Repo - Source code

🚀 Quick Start

# Install from PyPI (latest published release)
pip install inceptbench

# Or install from source (current repo snapshot)
git clone https://github.com/incept-ai/inceptbench.git
cd inceptbench
python3 -m venv venv && source venv/bin/activate
pip install -e .

# Create .env file (optional - for API-based evaluation)
echo "OPENAI_API_KEY=your_key" >> .env
echo "ANTHROPIC_API_KEY=your_key" >> .env

# Generate example
inceptbench example

# Run evaluation via CLI
inceptbench evaluate qs.json --full

# Or call the CLI module directly (no install needed)
PYTHONPATH="$(pwd)/src:$PYTHONPATH" python -m inceptbench.cli evaluate qs.json --full

✨ Features

• 6 Specialized Evaluators - Quality assessment across multiple dimensions
• Automatic Image Evaluation - Context-aware DI rubric scoring
• Parallel Processing - 47+ tasks running concurrently
• Multi-language Support - Evaluate content in any language
• Dual Content Types - Questions (MCQ/fill-in) and text content (passages/explanations)
• Production-Ready - Full demo in qs.json (~3-4 minutes)

📊 Evaluators

Evaluator	Type	Auto
ti_question_qa	Question quality (10 dimensions)	Yes
answer_verification	Answer correctness	Yes
reading_question_qc	MCQ distractor analysis	Yes
math_content_evaluator	Content quality (9 criteria)	Yes
text_content_evaluator	Pedagogical text assessment	Yes
image_quality_di_evaluator	DI rubric image quality	Auto
external_edubench	Educational benchmark (6 tasks)	No

See EVALUATORS.md for details.

📦 Architecture

inceptbench/
├── src/inceptbench/          # Unified package (src/ layout)
│   ├── orchestrator.py        # Main evaluation orchestrator
│   ├── cli.py                 # Command-line interface
│   ├── core/                  # Core evaluators and utilities
│   ├── agents/                # Agent-based evaluators
│   ├── qc/                    # Quality control modules
│   ├── evaluation/            # Evaluation templates
│   └── image/                 # Image quality evaluation
├── submodules/                # External dependencies
│   ├── reading-question-qc/
│   ├── EduBench/
│   ├── agentic-incept-reasoning/
│   └── image_generation_package/
└── pyproject.toml             # Package configuration

🎯 Demo

The qs.json file demonstrates all capabilities:

• 8 questions (MCQ/fill-in, Arabic/English)
• 4 text content items
• 7 images (auto-evaluated)
• All 6 evaluators active
• ~3-4 minute runtime

# Using CLI (recommended)
inceptbench evaluate qs.json --full

# Or using Python API
python -c "from inceptbench import universal_unified_benchmark, UniversalEvaluationRequest; import json; data = json.load(open('qs.json')); request = UniversalEvaluationRequest(**data); result = universal_unified_benchmark(request); print(result.model_dump_json(indent=2))"

📝 Example Usage

CLI

inceptbench evaluate qs.json --full
inceptbench evaluate qs.json -o results.json

Python API

from inceptbench import universal_unified_benchmark, UniversalEvaluationRequest

request = UniversalEvaluationRequest(
    submodules_to_run=["ti_question_qa", "answer_verification"],
    generated_questions=[{
        "id": "q1",
        "type": "mcq",
        "question": "What is 2+2?",
        "answer": "4",
        "answer_options": {"A": "3", "B": "4", "C": "5"},
        "answer_explanation": "2+2 equals 4",
        "skill": {
            "title": "Basic Addition",
            "grade": "1",
            "subject": "mathematics",
            "difficulty": "easy"
        }
    }]
)

response = universal_unified_benchmark(request)
print(response.evaluations["q1"].score)

See USAGE.md for complete examples.

🖼️ Image Evaluation

Add image_url to any question or content:

{
  "id": "q1",
  "question": "How many apples?",
  "image_url": "https://example.com/apples.png"
}

The image_quality_di_evaluator runs automatically with:

• Context-aware evaluation (accompaniment vs standalone)
• DI rubric scoring (0-100, normalized to 0-1)
• Hard-fail gates (answer leakage, wrong representations)
• Canonical DI representation checks

📥 Input Format

Questions:

{
  "submodules_to_run": ["ti_question_qa"],
  "generated_questions": [{
    "id": "q1",
    "type": "mcq",
    "question": "...",
    "answer": "...",
    "image_url": "..."  // Optional
  }]
}

Text Content:

{
  "submodules_to_run": ["text_content_evaluator"],
  "generated_content": [{
    "id": "text1",
    "type": "text",
    "content": "...",
    "image_url": "..."  // Optional
  }]
}

See INPUT_OUTPUT.md for complete schema.

📤 Output Format

Simplified (default):

{
  "evaluations": {
    "q1": {"score": 0.89}
  }
}

Full (verbose=True):

{
  "evaluations": {
    "q1": {
      "ti_question_qa": {
        "overall": 0.95,
        "scores": {...},
        "issues": [...],
        "strengths": [...]
      },
      "score": 0.89
    }
  }
}

🔄 Module Selection

Automatic (if submodules_to_run not specified):

• Questions → ti_question_qa, answer_verification, math_content_evaluator, reading_question_qc
• Text → text_content_evaluator, math_content_evaluator
• Images → image_quality_di_evaluator (auto-added)

Manual:

request = UniversalEvaluationRequest(
    submodules_to_run=["ti_question_qa", "answer_verification"],  # Only these
    generated_questions=[...]
)

📜 License

Proprietary - Copyright Trilogy Education Services