Datasets:

abdullah-alamodi
/

aqeedah-rag-dataset

@@ -1,315 +1,373 @@
----
-license: mit
-task_categories:
-- question-answering
-- text-retrieval
-language:
-- ar
-tags:
-- aqeedah
-- islamic-theology
-- arabic
-- rag
-- faiss
-- hybrid-search
-size_categories:
-- n<1K
-pretty_name: Aqeedah RAG Dataset
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: content
-    dtype: string
-  - name: meta
-    struct:
-    - name: author_name
-      dtype: string
-    - name: doc_name
-      dtype: string
-    - name: paragraph_number
-      dtype: int64
-  - name: embeddings
-    list: float64
-  splits:
-  - name: train
-    num_bytes: 43231133
-    num_examples: 5419
-  download_size: 30017136
-  dataset_size: 43231133
----
-<div align="center">
-  <img src="https://upload.wikimedia.org/wikipedia/en/a/ae/Najran_University_Logo.svg" alt="Najran University Logo" width="200"/>
-  # Aqeedah RAG Dataset 📚
-  **A Research Initiative by Najran University, Kingdom of Saudi Arabia**
-</div>
----
-# Aqeedah RAG Dataset 📚
-A curated Arabic Islamic theology (Aqeedah) dataset with pre-computed FAISS embeddings, designed for advanced Retrieval-Augmented Generation (RAG) applications in Islamic scholarly research.
-## 📋 Dataset Description
-This dataset represents a specialized collection of 5419 paragraphs from authoritative Islamic theology texts, meticulously compiled and structured for computational analysis. The corpus focuses specifically on Aqeedah (Islamic creed), covering foundational topics in Islamic belief and theology.
-**Key Features:**
-- **Authentic Arabic content** with complete diacritics (Tashkeel) preserved for linguistic accuracy
-- **Pre-computed semantic embeddings** (768-dimensional dense vectors) using state-of-the-art Arabic language models
-- **Rich scholarly metadata** including source document names, author attributions, and precise paragraph references
-- **Optimized FAISS index** for millisecond-scale semantic similarity search
-- **Hybrid retrieval support** combining traditional keyword-based (BM25) and modern neural approaches
-## 🎓 Research Context
-This dataset was developed as part of an academic research initiative at **Najran University, Kingdom of Saudi Arabia**, under the supervision of **Dr. Alya Alamodi**, a distinguished scholar holding a Ph.D. in Islamic Theology (Aqeedah). Dr. Alamodi's expertise in classical Islamic sciences combined with modern computational approaches has shaped the careful curation and theological accuracy of this corpus.
-The technical implementation and AI infrastructure were designed and developed by **Abdullah Alamodi**, M.Sc. candidate in Artificial Intelligence at **IU International University of Applied Sciences, Germany**. This collaboration represents an interdisciplinary effort bridging traditional Islamic scholarship with cutting-edge natural language processing and information retrieval technologies.
-### Research Objectives
-1. **Democratizing Access**: Making authoritative Aqeedah knowledge computationally accessible for researchers and students
-2. **Semantic Search**: Enabling meaning-based retrieval beyond keyword matching in classical Arabic texts
-3. **AI-Assisted Learning**: Supporting intelligent question-answering systems for Islamic education
-4. **Scholarly Validation**: Establishing benchmarks for Arabic NLP in religious domain-specific applications
-## 📚 Source Texts
-This dataset comprises carefully selected paragraphs from the following authoritative Islamic theology works:
-1. **شرح الطحاوية** (Sharh al-Tahawiyyah) - صدر الدين محمد بن علاء الدين علي بن محمد ابن أبي العز الحنفي (Volumes 1-2)
-2. **كتاب التوحيد** (Kitab al-Tawhid) - محمد بن عبد الوهاب
-3. **شرح العقيدة الواسطية** (Sharh al-Aqidah al-Wasitiyyah) - محمد بن صالح بن محمد العثيمين (Volumes 1-2)
-4. **القول المفيد على كتاب التوحيد** (Al-Qawl al-Mufid 'ala Kitab al-Tawhid) - محمد بن صالح بن محمد العثيمين (Volumes 1-4)
-5. **القول السديد شرح كتاب التوحيد** (Al-Qawl al-Sadid Sharh Kitab al-Tawhid) - عبد الرحمن بن ناصر السعدي
-6. **أصول الإيمان** (Usul al-Iman) - عبد العزيز بن عبد الله بن باز
-7. **الوجيز في عقيدة السلف الصالح أهل السنة والجماعة** (Al-Wajiz fi Aqidah al-Salaf al-Salih) - عبد الله بن عبد الحميد الأثري
-8. **الإسلام أصوله ومبادئه** (Al-Islam: Usuluhu wa Mabadi'uhu) - محمد بن عبد الله بن صالح السحيم
-9. **فتاوى نور على الدرب** (Fatawa Nur 'ala al-Darb) - محمد بن صالح بن محمد العثيمين (Volumes 1-4)
-## 🗂️ Dataset Structure
-### Data Fields
-- `paragraph_text` (string): The Arabic text content with complete diacritical marks
-- `doc_name` (string): Title of the source Islamic text
-- `author_name` (string): Name of the classical or contemporary scholar
-- `paragraph_number` (int): Sequential paragraph identifier within the source document
-- `embeddings` (list of float): Pre-computed 768-dimensional embedding vector (L2-normalized)
-### Data Splits
-This dataset contains a single split with 5419 carefully selected paragraphs from verified Islamic theology sources.
-## 🤖 Embedding Model
-**Model**: [`aubmindlab/bert-base-arabertv02`](https://huggingface.co/aubmindlab/bert-base-arabertv02)
-**Technical Specifications**:
-- Architecture: BERT-Base (12 layers, 768 hidden dimensions)
-- Pre-training: Arabic Wikipedia + other Arabic corpora
-- Embedding Dimension: 768
-- Text Normalization: Light preprocessing (preserves diacritics for theological accuracy)
-- Pooling Strategy: Attention-masked average pooling
-- Vector Normalization: L2 normalization for cosine similarity compatibility
-## 🚀 Usage
-### Installation
-```bash
-pip install datasets faiss-cpu torch transformers pyarabic rank-bm25
-```
-### Quick Start
-```python
-from datasets import load_dataset
-import torch
-from transformers import AutoTokenizer, AutoModel
-import pyarabic.araby as araby
-# Load dataset with FAISS index
-dataset = load_dataset("abdullah-alamodi/aqeedah-rag-dataset")
-# Load the embedding model
-model_name = "aubmindlab/bert-base-arabertv02"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModel.from_pretrained(model_name)
-# Add FAISS index for fast retrieval
-dataset['train'].add_faiss_index(column="embeddings")
-# Helper function for embedding
-def get_embedding(text):
-    normalized = araby.normalize_hamza(text)
-    text_input = f"query: {normalized}"
-    inputs = tokenizer([text_input], padding=True, truncation=True,
-                      max_length=512, return_tensors='pt')
-    with torch.no_grad():
-        outputs = model(**inputs)
-    # Average pooling
-    embeddings = outputs.last_hidden_state.mean(dim=1)
-    embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
-    return embeddings[0].numpy()
-# Search example
-query = "ما معنى شهادة أن لا إله إلا الله؟"
-query_embedding = get_embedding(query)
-# Find top 5 similar documents
-scores, retrieved = dataset['train'].get_nearest_examples(
-    "embeddings",
-    query_embedding,
-    k=5
-)
-# Display results
-for score, text, doc, author in zip(
-    scores,
-    retrieved['paragraph_text'],
-    retrieved['doc_name'],
-    retrieved['author_name']
-):
-    print(f"Score: {score:.4f}")
-    print(f"Document: {doc} by {author}")
-    print(f"Text: {text[:200]}...")
-    print("-" * 80)
-```
-### Hybrid Search (BM25 + Dense)
-```python
-from rank_bm25 import BM25Okapi
-import numpy as np
-import pyarabic.araby as araby
-# Prepare BM25 index
-def normalize_for_bm25(text):
-    text = araby.normalize_hamza(text)
-    text = araby.strip_diacritics(text)
-    text = araby.strip_tatweel(text)
-    return text
-corpus = [normalize_for_bm25(doc['paragraph_text']) for doc in dataset['train']]
-tokenized = [doc.split() for doc in corpus]
-bm25 = BM25Okapi(tokenized)
-# Search function
-def hybrid_search(query, top_k=5):
-    # BM25 search
-    norm_query = normalize_for_bm25(query)
-    bm25_scores = bm25.get_scores(norm_query.split())
-    bm25_top = np.argsort(bm25_scores)[::-1][:top_k]
-    # Dense search
-    query_emb = get_embedding(query)
-    scores, faiss_results = dataset['train'].get_nearest_examples(
-        "embeddings", query_emb, k=top_k
-    )
-    # Combine results using rank fusion
-    combined_results = # Your fusion logic here
-    return combined_results
-# Example usage
-results = hybrid_search("ما هي أركان الإيمان؟", top_k=5)
-```
-## 📊 Dataset Statistics
-- **Total paragraphs**: 5419
-- **Language**: Classical and Modern Standard Arabic (ar)
-- **Domain**: Islamic Theology (Aqeedah)
-- **Source texts**: 17 volumes from 9 distinct scholarly works
-- **Average text length**: ~951 characters per paragraph
-- **Embedding coverage**: 100% of corpus
-## 🎯 Intended Use
-### Primary Applications
-- ✅ **Scholarly RAG Systems**: Building question-answering systems for Islamic theology education
-- ✅ **Semantic Search**: Enabling meaning-based retrieval in classical Arabic religious texts
-- ✅ **Educational Technology**: Supporting AI-powered learning platforms for Aqeedah studies
-- ✅ **Research Tools**: Facilitating computational analysis of Islamic theological discourse
-### Research Domains
-- Arabic Natural Language Processing (NLP)
-- Information Retrieval in Religious Texts
-- Cross-lingual Semantic Search
-- Domain-Specific Language Models
-## ⚠️ Limitations & Considerations
-### Scope Limitations
-- **Domain Specificity**: Exclusively focused on Islamic theology (Aqeedah); not suitable for general Arabic NLP tasks
-- **Language**: Limited to Arabic; no multilingual support
-- **Corpus Size**: 5419 paragraphs represent a focused collection, not exhaustive coverage of all Aqeedah literature
-- **Temporal Coverage**: Focuses on established scholarly works; may not include the most recent publications
-### Theological Considerations
-- This dataset is curated for academic and educational purposes
-- Users should consult qualified Islamic scholars for authoritative religious guidance
-- The dataset represents specific theological perspectives within Sunni Islamic tradition (Ahl al-Sunnah wa al-Jama'ah)
-### Technical Limitations
-- Embeddings are model-specific (AraBERT v2); transfer to other models may require re-encoding
-- FAISS index optimized for CPU inference; GPU acceleration requires additional configuration
-- Diacritic preservation may affect compatibility with some NLP tools trained on non-diacritized text
-## 📜 License
-**MIT License** - This dataset is freely available for academic research, educational purposes, and commercial applications with proper attribution.
-## 🙏 Citation
-If you use this dataset in your research or applications, please cite:
-```bibtex
-@dataset{aqeedah_rag_dataset_2025,
-  title={Aqeedah RAG Dataset: Arabic Islamic Theology Corpus with Pre-computed Embeddings},
-  author={Alamodi, Alya and Alamodi, Abdullah},
-  year={2025},
-  institution={Najran University, Saudi Arabia},
-  publisher={Hugging Face},
-  howpublished={\url{https://huggingface.co/datasets/abdullah-alamodi/aqeedah-rag-dataset}},
-  note={Curated by Dr. Alya Alamodi (Najran University), Technical Implementation by Abdullah Alamodi (IU International University of Applied Sciences)}
-}
-```
-## 👥 Contributors
-**Principal Investigator & Theological Curation:**
-**Dr. Alya Alamodi**
-Ph.D. in Islamic Theology (Aqeedah)
-Najran University, Kingdom of Saudi Arabia
-**Technical Development & AI Implementation:**
-**Abdullah Alamodi**
-M.Sc. Candidate in Artificial Intelligence
-IU International University of Applied Sciences, Germany
-## 📧 Contact
-For questions regarding:
-- **Theological content and scholarly interpretation**: Contact Dr. Alya Alamodi via Najran University
-- **Technical implementation and AI methodology**: Contact Abdullah Alamodi
-- **General inquiries**: Open an issue on the dataset repository
----
-**Acknowledgments**: This work was supported by the academic resources of Najran University and developed with computational infrastructure provided by IU International University of Applied Sciences.

+---
+license: mit
+task_categories:
+- question-answering
+- text-retrieval
+language:
+- ar
+tags:
+- aqeedah
+- islamic-theology
+- arabic
+- rag
+- faiss
+- hybrid-search
+size_categories:
+- n<1K
+pretty_name: Aqeedah RAG Dataset
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: content
+    dtype: string
+  - name: meta
+    struct:
+    - name: author_name
+      dtype: string
+    - name: doc_name
+      dtype: string
+    - name: paragraph_number
+      dtype: int64
+  - name: embeddings
+    list: float64
+  splits:
+  - name: train
+    num_bytes: 43231133
+    num_examples: 5419
+  download_size: 30017136
+  dataset_size: 43231133
+---
+<div align="center">
+  <img src="https://upload.wikimedia.org/wikipedia/en/a/ae/Najran_University_Logo.svg" alt="Najran University Logo" width="200"/>
+  # Aqeedah RAG Dataset 📚
+  **A Research Initiative by Najran University, Kingdom of Saudi Arabia**
+</div>
+---
+# Aqeedah RAG Dataset 📚
+A curated Arabic Islamic theology (Aqeedah) dataset with pre-computed FAISS embeddings, designed for advanced Retrieval-Augmented Generation (RAG) applications in Islamic scholarly research.
+## 📋 Dataset Description
+This dataset represents a specialized collection of 5419 paragraphs from authoritative Islamic theology texts, meticulously compiled and structured for computational analysis. The corpus focuses specifically on Aqeedah (Islamic creed), covering foundational topics in Islamic belief and theology.
+**Key Features:**
+- **Authentic Arabic content** with complete diacritics (Tashkeel) preserved for linguistic accuracy
+- **Pre-computed semantic embeddings** (768-dimensional dense vectors) using state-of-the-art Arabic language models
+- **Rich scholarly metadata** including source document names, author attributions, and precise paragraph references
+- **Optimized FAISS index** for millisecond-scale semantic similarity search
+- **Hybrid retrieval support** combining traditional keyword-based (BM25) and modern neural approaches
+## 🎓 Research Context
+This dataset was developed as part of an academic research initiative at **Najran University, Kingdom of Saudi Arabia**, under the supervision of **Dr. Alya Alamodi**, a distinguished scholar holding a Ph.D. in Islamic Theology (Aqeedah). Dr. Alamodi's expertise in classical Islamic sciences combined with modern computational approaches has shaped the careful curation and theological accuracy of this corpus.
+The technical implementation and AI infrastructure were designed and developed by **Abdullah Alamodi**, M.Sc. candidate in Artificial Intelligence at **IU International University of Applied Sciences, Germany**. This collaboration represents an interdisciplinary effort bridging traditional Islamic scholarship with cutting-edge natural language processing and information retrieval technologies.
+### Research Objectives
+1. **Democratizing Access**: Making authoritative Aqeedah knowledge computationally accessible for researchers and students
+2. **Semantic Search**: Enabling meaning-based retrieval beyond keyword matching in classical Arabic texts
+3. **AI-Assisted Learning**: Supporting intelligent question-answering systems for Islamic education
+4. **Scholarly Validation**: Establishing benchmarks for Arabic NLP in religious domain-specific applications
+## 📚 Source Texts
+This dataset comprises carefully selected paragraphs from the following authoritative Islamic theology works:
+1. **شرح الطحاوية** (Sharh al-Tahawiyyah) - صدر الدين محمد بن علاء الدين علي بن محمد ابن أبي العز الحنفي (Volumes 1-2)
+2. **كتاب التوحيد** (Kitab al-Tawhid) - محمد بن عبد الوهاب
+3. **شرح العقيدة الواسطية** (Sharh al-Aqidah al-Wasitiyyah) - محمد بن صالح بن محمد العثيمين (Volumes 1-2)
+4. **القول المفيد على كتاب التوحيد** (Al-Qawl al-Mufid 'ala Kitab al-Tawhid) - محمد بن صالح بن محمد العثيمين (Volumes 1-4)
+5. **القول السديد شرح كتاب التوحيد** (Al-Qawl al-Sadid Sharh Kitab al-Tawhid) - عبد الرحمن بن ناصر السعدي
+6. **أصول الإيمان** (Usul al-Iman) - عبد العزيز بن عبد الله بن باز
+7. **الوجيز في عقيدة السلف الصالح أهل السنة والجماعة** (Al-Wajiz fi Aqidah al-Salaf al-Salih) - عبد الله بن عبد الحميد الأثري
+8. **الإسلام أصوله ومبادئه** (Al-Islam: Usuluhu wa Mabadi'uhu) - محمد بن عبد الله بن صالح السحيم
+9. **فتاوى نور على الدرب** (Fatawa Nur 'ala al-Darb) - محمد بن صالح بن محمد العثيمين (Volumes 1-4)
+## 🗂️ Dataset Structure
+### Data Fields
+- `paragraph_text` (string): The Arabic text content with complete diacritical marks
+- `doc_name` (string): Title of the source Islamic text
+- `author_name` (string): Name of the classical or contemporary scholar
+- `paragraph_number` (int): Sequential paragraph identifier within the source document
+- `embeddings` (list of float): Pre-computed 768-dimensional embedding vector (L2-normalized)
+### Data Splits
+This dataset contains a single split with 5419 carefully selected paragraphs from verified Islamic theology sources.
+## 🤖 Embedding Model
+**Model**: [`aubmindlab/bert-base-arabertv02`](https://huggingface.co/aubmindlab/bert-base-arabertv02)
+**Technical Specifications**:
+- Architecture: BERT-Base (12 layers, 768 hidden dimensions)
+- Pre-training: Arabic Wikipedia + other Arabic corpora
+- Embedding Dimension: 768
+- Text Normalization: Light preprocessing (preserves diacritics for theological accuracy)
+- Pooling Strategy: Attention-masked average pooling
+- Vector Normalization: L2 normalization for cosine similarity compatibility
+## 🚀 Usage
+### Installation
+```bash
+pip install datasets faiss-cpu torch transformers pyarabic rank-bm25
+```
+### Quick Start
+```python
+from datasets import load_dataset
+import torch
+from transformers import AutoTokenizer, AutoModel
+import pyarabic.araby as araby
+# Load dataset with FAISS index
+dataset = load_dataset("abdullah-alamodi/aqeedah-rag-dataset")
+# Load the embedding model
+model_name = "aubmindlab/bert-base-arabertv02"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+# Add FAISS index for fast retrieval
+dataset['train'].add_faiss_index(column="embeddings")
+# Helper function for embedding
+def get_embedding(text):
+    normalized = araby.normalize_hamza(text)
+    text_input = f"query: {normalized}"
+    inputs = tokenizer([text_input], padding=True, truncation=True,
+                      max_length=512, return_tensors='pt')
+    with torch.no_grad():
+        outputs = model(**inputs)
+    # Average pooling
+    embeddings = outputs.last_hidden_state.mean(dim=1)
+    embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+    return embeddings[0].numpy()
+# Search example
+query = "ما معنى شهادة أن لا إله إلا الله؟"
+query_embedding = get_embedding(query)
+# Find top 5 similar documents
+scores, retrieved = dataset['train'].get_nearest_examples(
+    "embeddings",
+    query_embedding,
+    k=5
+)
+# Display results
+for i, (score, text, meta) in enumerate(zip(
+    scores,
+    retrieved['content'],
+    retrieved['meta']
+)):
+    print(f"النص المسترجع للسؤال {i+1}".center(80, '-'))
+    print(f"Score: {score:.4f}")
+    print(f"Document: {meta['doc_name']} by {meta['author_name']}")
+    print(f"Paragraph: {meta['paragraph_number']}")
+    print(f"Text: {text[:200]}...")
+    print("\n")
+```
+### Hybrid Search (BM25 + Dense)
+```python
+from rank_bm25 import BM25Okapi
+import numpy as np
+import pyarabic.araby as araby
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModel
+import torch
+# Load dataset with FAISS index
+dataset = load_dataset("abdullah-alamodi/aqeedah-rag-dataset")
+# Load the embedding model
+model_name = "aubmindlab/bert-base-arabertv02"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+# Add FAISS index for fast retrieval
+dataset['train'].add_faiss_index(column="embeddings")
+# Helper function for embedding
+def get_embedding(text):
+    normalized = araby.normalize_hamza(text)
+    text_input = f"query: {normalized}"
+    inputs = tokenizer([text_input], padding=True, truncation=True,
+                      max_length=512, return_tensors='pt')
+    with torch.no_grad():
+        outputs = model(**inputs)
+    # Average pooling
+    embeddings = outputs.last_hidden_state.mean(dim=1)
+    embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+    return embeddings[0].numpy()
+# Prepare BM25 index
+def normalize_for_bm25(text):
+    text = araby.normalize_hamza(text)
+    text = araby.strip_diacritics(text)
+    text = araby.strip_tatweel(text)
+    return text
+corpus = [normalize_for_bm25(doc['content']) for doc in dataset['train']]
+tokenized = [doc.split() for doc in corpus]
+bm25 = BM25Okapi(tokenized)
+# Search function
+def hybrid_search(query, top_k=5):
+    # BM25 search
+    norm_query = normalize_for_bm25(query)
+    bm25_scores = bm25.get_scores(norm_query.split())
+    bm25_top = np.argsort(bm25_scores)[::-1][:top_k]
+    # Dense search
+    query_emb = get_embedding(query)
+    scores, faiss_results = dataset['train'].get_nearest_examples(
+        "embeddings", query_emb, k=top_k
+    )
+    # Extract FAISS indices (they're already sorted by score)
+    # Since get_nearest_examples returns actual data, we need to track indices differently
+    # Simple approach: just combine the unique results
+    # Get unique indices from both methods
+    bm25_indices = set(bm25_top.tolist())
+    # For FAISS, we'll use the returned results directly
+    # Combine: prioritize FAISS results, then add BM25-only results
+    combined_results = []
+    seen_content = set()
+    # Add FAISS results first
+    for content, meta in zip(faiss_results['content'], faiss_results['meta']):
+        if content not in seen_content:
+            combined_results.append({'content': content, 'meta': meta})
+            seen_content.add(content)
+    # Add unique BM25 results
+    for idx in bm25_top:
+        doc = dataset['train'][int(idx)]
+        if doc['content'] not in seen_content:
+            combined_results.append(doc)
+            seen_content.add(doc['content'])
+            if len(combined_results) >= top_k * 2:  # Get up to 2x results
+                break
+    return combined_results[:top_k * 2]  # Return more results for better coverage
+# Example usage
+results = hybrid_search("ما هي أركان الإيمان؟", top_k=5)
+for i, res in enumerate(results):
+    print(f"Result {i+1}: {res['content']}\n")
+```
+## 📊 Dataset Statistics
+- **Total paragraphs**: 5419
+- **Language**: Classical and Modern Standard Arabic (ar)
+- **Domain**: Islamic Theology (Aqeedah)
+- **Source texts**: 17 volumes from 9 distinct scholarly works
+- **Average text length**: ~951 characters per paragraph
+- **Embedding coverage**: 100% of corpus
+## 🎯 Intended Use
+### Primary Applications
+- ✅ **Scholarly RAG Systems**: Building question-answering systems for Islamic theology education
+- ✅ **Semantic Search**: Enabling meaning-based retrieval in classical Arabic religious texts
+- ✅ **Educational Technology**: Supporting AI-powered learning platforms for Aqeedah studies
+- ✅ **Research Tools**: Facilitating computational analysis of Islamic theological discourse
+### Research Domains
+- Arabic Natural Language Processing (NLP)
+- Information Retrieval in Religious Texts
+- Cross-lingual Semantic Search
+- Domain-Specific Language Models
+## ⚠️ Limitations & Considerations
+### Scope Limitations
+- **Domain Specificity**: Exclusively focused on Islamic theology (Aqeedah); not suitable for general Arabic NLP tasks
+- **Language**: Limited to Arabic; no multilingual support
+- **Corpus Size**: 5419 paragraphs represent a focused collection, not exhaustive coverage of all Aqeedah literature
+- **Temporal Coverage**: Focuses on established scholarly works; may not include the most recent publications
+### Theological Considerations
+- This dataset is curated for academic and educational purposes
+- Users should consult qualified Islamic scholars for authoritative religious guidance
+- The dataset represents specific theological perspectives within Sunni Islamic tradition (Ahl al-Sunnah wa al-Jama'ah)
+### Technical Limitations
+- Embeddings are model-specific (AraBERT v2); transfer to other models may require re-encoding
+- FAISS index optimized for CPU inference; GPU acceleration requires additional configuration
+- Diacritic preservation may affect compatibility with some NLP tools trained on non-diacritized text
+## 📜 License
+**MIT License** - This dataset is freely available for academic research, educational purposes, and commercial applications with proper attribution.
+## 🙏 Citation
+If you use this dataset in your research or applications, please cite:
+```bibtex
+@dataset{aqeedah_rag_dataset_2025,
+  title={Aqeedah RAG Dataset: Arabic Islamic Theology Corpus with Pre-computed Embeddings},
+  author={Alamodi, Alya and Alamodi, Abdullah},
+  year={2025},
+  institution={Najran University, Saudi Arabia},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/datasets/abdullah-alamodi/aqeedah-rag-dataset}},
+  note={Curated by Dr. Alya Alamodi (Najran University), Technical Implementation by Abdullah Alamodi (IU International University of Applied Sciences)}
+}
+```
+## 👥 Contributors
+**Principal Investigator & Theological Curation:**
+**Dr. Alya Alamodi**
+Ph.D. in Islamic Theology (Aqeedah)
+Najran University, Kingdom of Saudi Arabia
+**Technical Development & AI Implementation:**
+**Abdullah Alamodi**
+M.Sc. Candidate in Artificial Intelligence
+IU International University of Applied Sciences, Germany
+## 📧 Contact
+For questions regarding:
+- **Theological content and scholarly interpretation**: Contact Dr. Alya Alamodi via Najran University
+- **Technical implementation and AI methodology**: Contact Abdullah Alamodi
+- **General inquiries**: Open an issue on the dataset repository
+---
+**Acknowledgments**: This work was supported by the academic resources of Najran University and developed with computational infrastructure provided by IU International University of Applied Sciences.