🏛️ 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.
🌍 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.
📦 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']}")
🛠️ API Reference
| Method | Parameters | Return Type | Description |
|---|---|---|---|
search_by_text() |
text_query: str, top_k: int = 5 |
List[Dict] |
Executes deterministic token retrieval with ASCII-folding (0.9600) & exact diacritic parity (1.0000). |
search_by_vision() |
image_input: Union[str, Image.Image], top_k: int = 5, ood_threshold: float = 0.0600 |
Dict |
Cross-modal SigLIP visual search against pre-computed static embeddings with OOD noise rejection. |
🏛️ 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
1 maintainer
Metadata
1 maintainer
Release files for agba-engine 5.2.4
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Total release size: 5.6 MB
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