Absher Smart Assistant

🏛️ Sovereign AI for Saudi MOI Services | Version 1.0

🏛️ Sovereign AI • 🔎 Hybrid RAG • 🤖 Bilingual LLM • 🌍 Zero-Shot Cross-Lingual • 🎤 Speech-to-Speech

🏛️ Sovereign AI | 🤖 Large Language Models | 🔎 Hybrid RAG | 🛡️ GRC Compliant | 🌍 Multilingual Ready | 🎤 Speech & TTS

📖 Overview

Absher Smart Assistant is a sovereign AI conversational system designed to democratize access to Saudi Ministry of Interior (MOI) services. Addressing the critical challenges of language barriers and hallucinations in traditional LLMs, the system employs a novel Cross-Lingual Hybrid Retrieval-Augmented Generation (RAG) architecture to anchor generative capabilities to a curated, verified knowledge base of MOI regulations.

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✨ Advanced Technical Features

🧠 Sovereign Saudi Intelligence (ALLaM-7B)

Powered by ALLaM-7B-Instruct-preview, developed by SDAIA.

• Training Depth: Pretrained on 5.2 Trillion tokens (4T English + 1.2T Mixed Arabic/English).
• Optimization: Built on NVIDIA/MegatronLM with bf16-mixed precision, ensuring high MFU (~42%) during training.

🔍 Hybrid Retrieval with RRF Fusion

The system eliminates hallucinations by synergizing dense vector retrieval (BGE-M3) with sparse keyword matching (BM25). Results are fused using the Reciprocal Rank Fusion (RRF) algorithm:

$$RRF~Score(d) = \sum_{j \in {Dense, Sparse}} \frac{1}{k + r_j(d)}$$

Where $k=60$ is a smoothing constant to prioritize documents verified by both retrieval streams.

🌍 Zero-Shot Cross-Lingual Mechanism

Enables multilingual support (English, French, Russian, etc.) without an intermediate translation layer. By leveraging a unified embedding space, the system maps foreign queries directly to Arabic regulatory vectors, ensuring low-latency and preserving semantic nuance.

🛡️ Robust ETL & Self-Healing

• Advanced Normalization: Specialized NLP pipeline standardizes Arabic text (e.g., unifying Alef and Taa Marbuta forms) to resolve morphological inconsistencies.
• Smart Chunking: Employs a recursive character splitter with a 250-token overlap to preserve context across boundaries.
• Self-Healing Vector Store: A fail-safe mechanism that performs real-time sanity checks and automatically rebuilds the FAISS index upon detecting corruption.

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📊 Benchmark Results (v1.0)

Tested on NVIDIA A100 using a rigorous global benchmark across 6 core languages.

Metric	Result	Status
Arabic Semantic Accuracy	96.0%	✅ Superior (Native)
English Semantic Accuracy	88.0%	✅ Excellent
Hallucination Rate	0.0%	🛡️ Zero-Hallucination
Average Latency (Arabic)	2.10 sec	⚡ Ultra-Fast

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📜 Credits & Citations

Model Acknowledgment

This project utilizes the ALLaM model series by SDAIA. We acknowledge the National Center for Artificial Intelligence (NCAI) for their work on Arabic Language Technology.

@inproceedings{
    bari2025allam,
    title={{ALL}aM: Large Language Models for Arabic and English},
    author={M Saiful Bari and Yazeed Alnumay and others},
    booktitle={The Thirteenth International Conference on Learning Representations},
    year={2025},
    url={[https://openreview.net/forum?id=MscdsFVZrN](https://openreview.net/forum?id=MscdsFVZrN)}
}
}

📂 Project Structure

MOI_Universal_Assistant/
├── core/             # The Reasoning Engine (RAG Pipeline, Vector Store)
├── data/             # Data Layer (ETL Pipeline, KG, Schema Validation)
├── Benchmarks/       # The Audit Suite (Safety, Stress, Model Arena)
├── ui/               # Interface (Gradio App, Professional MOI Theme)
├── utils/            # Utilities (Neural TTS, Rotational Logger, NLP)
├── config.py         # Central Intelligence Configuration
└── main.py           # Production Entry Point

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🛠️ Installation & Execution

1. Prerequisites

• Hardware: NVIDIA GPU (A100/H100 Optimized recommended) with 80GB+ VRAM.
• Software: Python 3.9+, CUDA Toolkit.

2. Setup & Installation

# Clone the repository
git clone [https://github.com/Ahmed-alrashidi/MOI_ChatBot.git](https://github.com/Ahmed-alrashidi/MOI_ChatBot.git)
cd MOI_ChatBot

# Install dependencies
pip install -r requirements.txt

3. Configure Environment

export HF_TOKEN="your_hugging_face_token"

4. Launch System

The system handles automated hardware diagnostics and database builds on startup.

python main.py

📄 Academic Context

Developed as a final project for the CS299-Master's Directed Research course at
King Abdullah University of Science and Technology (KAUST) – 2026

Version: 1.0 (Stable Release)
Last Updated: Jan 2026