🏛️ Agba Engine (`agba-engine`)

Version: 5.1.8
Author & Lead Architect: Aruna Olanrewaju Kabiru
License: RAIL-Cultural-Heritage-v1.0

agba-engine is a multi-modal, diacritic-preserving retrieval framework engineered to index Global Cultural Heritage, Indigenous Scripts, and Tonal Orthographies without semantic stripping or unicode degradation.

🌍 The Mission: Preserving Global Heritage

Standard LLMs and vector search engines strip diacritics, flatten tonal markers, and misclassify rare indigenous vocabulary. Agba Engine provides an open, diacritic-proof retrieval architecture that preserves native orthographies in their full canonical purity.

Developers and cultural archivists globally can leverage agba-engine to register, index, and retrieve cultural assets across any language or script.

📦 Quickstart & Global Developer Workflow

Installation

pip install agba-engine==5.1.8

Registering Your Local Cultural Corpus

You can register and search any custom cultural dataset dynamically using register_corpus():

from agba_engine.core.engine import AgbaSearchEngine

# Initialize the engine
engine = AgbaSearchEngine()

# 1. Define your indigenous/cultural corpus entries
custom_heritage_data = [
    {
        "id": "Opón Ifá", 
        "class": "Divination Artefacts", 
        "description": "Sacred carved wooden tray used in Traditional Divination ceremonies."
    },
    {
        "id": "Ìroko", 
        "class": "Botany & Sacred Trees", 
        "description": "A large hardwood Tree from tropical Africa associated with Spiritual reverence."
    }
]

# 2. Register your corpus dynamically into the active search index
engine.register_corpus(custom_heritage_data)

# 3. Query using plain ASCII or diacritic-preserved text
results = engine.search_by_text("Opon Ifa", top_k=1)
print(f"Retrieved Entity: {results[0]['id']} ({results[0]['classification']})")
print(f"Description: {results[0]['description']}")
print(f"Confidence Score: {results[0]['score']}")

🛠️ API Reference

Method	Parameters	Return Type	Description
`register_corpus()`	`custom_list: List[Dict]`	`None`	Appends custom cultural JSON datasets to the active index.
`search_by_text()`	`query: str, top_k: int`	`List[Dict]`	Executes hybrid vector retrieval with ASCII-folding & diacritic parity.
`search_by_vision()`	`image_embedding, top_k: int`	`Dict`	Cross-modal visual search with Null Anchor thresholding.

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