ijaza 1.0.0

Validate and verify Quranic verses in LLM-generated text with high accuracy

Ijaza

Validate and verify Quranic verses in LLM-generated text with high accuracy.

Ijaza (Arabic: إجازة, meaning "authorization" or "permission to transmit") is a Python library that ensures the authenticity of Quranic text in AI-generated content. Just as traditional Islamic scholarship requires an ijaza to transmit sacred knowledge, this library provides a digital verification layer for Quranic quotes.

Motivation

Large Language Models (LLMs) frequently misquote Quranic verses — changing words, mixing verses, or even fabricating text that sounds Quranic but isn't. This is a serious concern for:

• Islamic content creators who need accurate Quranic citations
• Educational platforms teaching Quran and Islamic studies
• AI applications serving Muslim communities (chatbots, translation tools, khutbah assistants)
• Developers building LLM-powered tools that handle religious text

Ijaza catches these errors automatically, corrects misquotations, and ensures that every Quranic verse in your application is authentic.

Origin & Credits

This project began as a Python reimplementation of the excellent quran-validator npm package by Yazin Alirhayim. We needed the same functionality for our Python-based projects and decided to port it while adding features specific to our use case.

Ijaza was developed as part of the PolyKhateeb project — a real-time transcription and translation system for Islamic sermons (khutbahs). In that context, we needed to:

• Detect Quranic segments in transcribed speech to preserve them verbatim
• Validate LLM-corrected text to catch any misquotations
• Inject system prompts into LLMs to properly tag Quran quotes

Installation

pip install ijaza

For better fuzzy matching performance (optional):

pip install ijaza[performance]

Quick Start

Basic Validation

from ijaza import QuranValidator

validator = QuranValidator()

# Validate a specific quote
result = validator.validate("بِسْمِ ٱللَّهِ ٱلرَّحْمَٰنِ ٱلرَّحِيمِ")
print(result.is_valid)   # True
print(result.reference)  # "1:1"
print(result.match_type) # "exact"

LLM Integration (Recommended)

from ijaza import LLMProcessor, SYSTEM_PROMPTS

# 1. Add system prompt to your LLM call
system_prompt = SYSTEM_PROMPTS['xml']

# 2. Process LLM response
processor = LLMProcessor()
result = processor.process(llm_response)

# 3. Use corrected text
print(result.corrected_text)
print(result.all_valid)  # True if all quotes are authentic

# 4. Check for issues
for quote in result.quotes:
    if quote.was_corrected:
        print(f"Corrected: {quote.original} -> {quote.corrected}")

Features

• Multi-tier matching: exact → normalized → partial → fuzzy
• LLM integration: System prompts + post-processing validation
• Arabic normalization: Handles diacritics, alef variants, hamza, etc.
• Auto-correction: Fixes misquoted verses automatically
• Detection: Finds untagged Quran quotes in text
• Full database: 6,236 verses with Uthmani script
• Zero dependencies: Pure Python implementation (optional rapidfuzz for performance)

API Reference

QuranValidator

from ijaza import QuranValidator, ValidatorOptions

# With custom options
validator = QuranValidator(ValidatorOptions(
    fuzzy_threshold=0.85,
    max_suggestions=5,
    include_partial=True,
))

# Validate text
result = validator.validate("Arabic text here")

# Detect and validate all quotes in text
detection = validator.detect_and_validate("Text with Quran quotes...")

# Get specific verse
verse = validator.get_verse(surah=1, ayah=1)

# Get verse range
range_result = validator.get_verse_range(surah=112, start_ayah=1, end_ayah=4)

# Search verses
results = validator.search("search query", limit=10)

LLMProcessor

from ijaza import LLMProcessor, LLMProcessorOptions

processor = LLMProcessor(LLMProcessorOptions(
    auto_correct=True,
    min_confidence=0.85,
    scan_untagged=True,
    tag_format='xml',  # or 'markdown', 'bracket'
))

# Get system prompt for your LLM
prompt = processor.get_system_prompt()

# Process LLM output
result = processor.process(llm_output)

Normalization Utilities

from ijaza import (
    normalize_arabic,
    remove_diacritics,
    contains_arabic,
    extract_arabic_segments,
    calculate_similarity,
)

# Normalize Arabic text
normalized = normalize_arabic("بِسْمِ اللَّهِ")  # "بسم الله"

# Remove only diacritics
clean = remove_diacritics("السَّلَامُ")  # "السلام"

# Check for Arabic content
has_arabic = contains_arabic("Hello مرحبا")  # True

# Extract Arabic segments from mixed text
segments = extract_arabic_segments("The verse بسم الله means...")

# Calculate text similarity
similarity = calculate_similarity("text1", "text2")  # 0.0 - 1.0

Future Work

Translation Validation

Include major English translations (Sahih International, Pickthall, Yusuf Ali) and validate that translations correspond to the correct Arabic source verse.

Framework Integrations

• LangChain / LlamaIndex guardrails
• FastAPI middleware
• Streamlit components
• Django/Flask integration

Performance Optimizations

• N-gram indexing for pre-filtering
• BK-tree for metric-space nearest-neighbor search
• Async/streaming support for real-time pipelines

Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.

License

MIT

Acknowledgments

• Yazin Alirhayim for the original quran-validator npm package
• AlQuran.cloud for the Quran API
• The PolyKhateeb project team