agba-engine

🏛️ Agba Engine (agba-engine)

Version: 5.2.4
Author & Lead Architect: Aruna Olanrewaju Kabiru
License: RAIL-Cultural-Heritage-v1.0

agba-engine is a cross-modal vision and diacritic-preserving text retrieval framework engineered to index Global Cultural Heritage, Indigenous Scripts, and Tonal Orthographies without semantic stripping, Unicode degradation, or sub-token drift.

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🌍 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 dual-retrieval architecture that preserves native orthographies in their full canonical purity while integrating neural zero-shot vision search.

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📦 Quickstart & Global Developer Workflow

Installation

pip install agba-engine==5.2.4

Initializing Engine & Executing Searches

from agba_engine import AgbaProductionEngine

# Initialize dual engine (loads bundled ground-truth corpus & static SigLIP embeddings)
engine = AgbaProductionEngine()

# 1. Deterministic Text Retrieval (Diacritic & ASCII-Folded Parity)
text_results = engine.search_by_text("Opon Ifa", top_k=1)
print(f"Retrieved Entity: {text_results[0]['id']} | Score: {text_results[0]['score']}")

# 2. Cross-Modal Neural Vision Search (SigLIP + OOD Noise Guardrail)
vision_results = engine.search_by_vision("https://example.com/yewa_beads.jpg", top_k=3, ood_threshold=0.0600)
if vision_results["status"] == "MATCH_FOUND":
    for hit in vision_results["results"]:
        print(f"Rank {hit['rank']}: {hit['id']} ({hit['class']}) — Cosine: {hit['raw_cosine']}")

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🛠️ API Reference

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🏛️ Enterprise & Institutional API Access (Museums & Archives)

agba-engine provides specialized integration hooks for cultural institutions, digital heritage archives, and global museum databases seeking high-fidelity index retrieval for native orthographies, 3D artifacts, and historical vocalization assets.

• Audio & Vocalization Pipeline: Endpoints for indexing pitch-contour models, tonal pronunciation vectors, and spoken indigenous archives.
• Museum Corpus Sync: Direct ingestion pipelines for institutional digital asset management (DAM) platforms.

For API access keys, custom corpus integration support, or institutional partnerships, reach out directly or open a request on our Hugging Face Space:

• GitHub Repository: Agba-Engine on GitHub
• Hugging Face Space: Agba Engine Live Demo & Community
• Direct Contact: only1mooseylion@gmail.com