ALLAM-RAG: A Context-Aware Saudi Arabic Conversational AI System for Alzheimer’s Support
Overview
ALLAM-RAG is a Saudi Arabic conversational AI system that combines Retrieval-Augmented Generation (RAG) with the ALLAM Large Language Model to provide context-aware assistance for Alzheimer's patients and caregivers.
The system retrieves relevant information from specialized knowledge bases using hybrid vector search and reranking before generating accurate, personalized responses through the ALLAM language model. By combining retrieval with language generation, the system minimizes hallucinations while delivering reliable and natural Arabic conversations tailored to the Saudi dialect.
Features
Saudi Arabic conversational assistant
Retrieval-Augmented Generation (RAG) architecture
Powered by the ALLAM Large Language Model
Hybrid semantic and keyword retrieval using Weaviate
Multilingual reranking using Cohere Rerank
Context-aware response generation
Alzheimer's patient support
General Saudi knowledge support
Real-time time and day awareness
Arabic text normalization and intent detection
System Architecture
User Query
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Query Classification
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Knowledge Base Selection
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Weaviate Hybrid Search
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Cohere Reranker
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Relevant Context
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ALLAM Language Model
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Generated Response
Technologies Used
Python
ALLAM-7B-Instruct
Transformers
Hugging Face
Weaviate Vector Database
Cohere Rerank API
NumPy
Knowledge Bases
The system utilizes two specialized knowledge bases:
This repository contains a public research version.
Private implementation details, API keys, deployment configurations,
and some internal pipeline components are excluded.
The complete implementation is maintained privately.
