---
license: cc-by-4.0
---

# 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.

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## 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

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## System Architecture

```
User Query
      │
      ▼
Query Classification
      │
      ▼
Knowledge Base Selection
      │
      ▼
Weaviate Hybrid Search
      │
      ▼
Cohere Reranker
      │
      ▼
Relevant Context
      │
      ▼
ALLAM Language Model
      │
      ▼
Generated Response
```

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## Technologies Used

- Python
- ALLAM-7B-Instruct
- Transformers
- Hugging Face
- Weaviate Vector Database
- Cohere Rerank API
- NumPy

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## Knowledge Bases

The system utilizes two specialized knowledge bases:

### Alzheimer's Knowledge Base

Contains information related to:

- Patient identity
- Memory assistance
- Medication reminders
- Daily activities
- Alzheimer's symptoms
- Caregiving guidance
- Orientation support

### General Knowledge Base

Contains Saudi Arabic information including:

- Islamic knowledge
- Saudi Arabia
- Culture
- Geography
- Public information

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## Retrieval Pipeline

The retrieval pipeline consists of:

1. Query preprocessing and normalization
2. Query classification
3. Knowledge base selection
4. Hybrid retrieval from Weaviate
5. Cohere multilingual reranking
6. Context selection
7. Response generation using ALLAM

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## Example Queries

### Alzheimer's Support

- مين أنا؟
- وين ساكن؟
- كيف حالتي الصحية؟
- نسيت أخذ الدواء
- ليه أنا هنا؟

## Project Structure

```
app.py
chatbot.py
rag_pipeline.py
weaviate_server.py
requirements.txt
README.md
```

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## Running Locally

Install dependencies:

```bash
pip install -r requirements.txt
```

Configure the required environment variables:

- COHERE_API_KEY
- HF_TOKEN
- WEAVIATE_URL (if applicable)
- WEAVIATE_API_KEY (if applicable)

Launch the application:

```bash
python app.py
```

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## System Architecture

![System Architecture](ALLAM_based_RAG_Architecture.png)



## Deployment

The project is designed for deployment using:

- Hugging Face Models
- ALLAM language model
- Weaviate Vector Database
- Cohere Language model
- 
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## Demo

![Responses Demo](demo.png)

## Future Improvements

- Multi-turn conversation memory
- Expanded healthcare knowledge base

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## Author

**Shahad Aljohani**

Recent Computer Science Undergraduate 

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## Privacy Notice

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.