Kandil7/shamela-vectors

🇬🇧 English

The largest open-source vector database of the complete al-Maktaba al-Shamela (المكتبة الشاملة) Islamic text corpus, prepared for semantic search and Retrieval-Augmented Generation (RAG). Includes the full Quran text (6,236 verses, Hafs 'an 'Asim).

Statistics:

• 11,482,164 chunks from 8,589 classical Islamic books
• 6,236 Quran verses (one verse = one chunk, included in total)
• 40 categories covering the full breadth of Islamic scholarship
• Period: 1st century AH to 15th century AH
• Language: Arabic (primary)

Schema is fully consistent with Maktabati/openiti-vectors — same field names, types, and conventions. Both datasets can be queried together seamlessly.

🔍 Live Demo

Try the search demo directly in your browser — no setup required:

👉 maktabati.ai · Shamela Search (coming soon)

Keyword search (author, title, full text). For semantic / vector search, import into Qdrant using the instructions below.

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🇸🇦 العربية

أكبر قاعدة بيانات متجهية مفتوحة المصدر للمكتبة الشاملة، تضمّ ما يزيد على 7 ملايين مقطع نصي مستخرج من 8,589 كتابًا من كتب التراث الإسلامي، مُعدَّة للبحث الدلالي وتطبيقات الذكاء الاصطناعي التوليدي (RAG).

تشمل المجموعة كامل القرآن الكريم برواية حفص عن عاصم (6,236 آية) — آية واحدة لكل مدخل — وهو ما يجعل الاسترجاع القرآني دقيقًا على مستوى الآية.

تم التضمين والفهرسة بالكامل على حاسوب شخصي دون أي خدمات سحابية، باستخدام نموذج intfloat/multilingual-e5-base بأبعاد 768 ومسافة كوساين.

الإحصاءات:

• 11,482,164 مقطعًا نصيًا من 8,589 كتابًا (منها 6,236 آية قرآنية)
• 6,236 آية قرآنية (آية = مقطع واحد)
• 40 تصنيفًا تغطي علوم الإسلام كاملةً
• النصوص من القرن الأول إلى الخامس عشر الهجري
• منجز على حاسوب شخصي — دون خدمات سحابية

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🇩🇪 Deutsch

Die größte Open-Source-Vektordatenbank des vollständigen al-Maktaba al-Shamela-Korpus islamischer Texte, vorbereitet für semantische Suche und Retrieval-Augmented Generation (RAG). Enthält den vollständigen Korantext (6.236 Verse, Hafs 'an Asim).

Statistiken:

• 11.482.164 Textabschnitte aus 8.589 klassischen islamischen Büchern (davon 6.236 Koranverse)
• 6.236 Koranverse (ein Vers = ein Abschnitt)
• 40 Kategorien — das gesamte Spektrum der islamischen Wissenschaften
• Zeitraum: 1. bis 15. Jahrhundert n. H.
• Sprache: Arabisch

Vollständig eingebettet und indiziert auf einem handelsüblichen Heimcomputer ohne Cloud-Dienste. Das Schema ist konsistent mit Maktabati/openiti-vectors.

🇹🇷 Türkçe

al-Maktaba al-Shamela İslami metin külliyatının tamamına ait en büyük açık kaynaklı vektör veritabanı; anlamsal arama ve Üretim Destekli Geri Getirme (RAG) için hazırlanmıştır. Tam Kur'an metnini içermektedir (6.236 ayet, Hafs rivayeti).

İstatistikler:

• 8.589 klasik İslami kitaptan 11.482.164 metin parçası (6.236 Kur'an ayeti dahil)
• 6.236 Kur'an ayeti (bir ayet = bir parça)
• 40 kategori — İslam ilimlerinin tüm alanlarını kapsar
• Dönem: Hicri 1. ila 15. yüzyıl
• Dil: Arapça

Bulut hizmeti kullanılmadan, tüketiciye yönelik bir bilgisayarda tamamen gömülü ve dizine eklenmiştir. Şema Maktabati/openiti-vectors ile tam uyumludur.

🇮🇷 فارسی

بزرگ‌ترین پایگاه داده برداری متن‌باز از مجموعه کامل المکتبة الشاملة، آماده‌شده برای جستجوی معنایی و تولید متن مبتنی بر بازیابی (RAG). شامل متن کامل قرآن کریم (۶٬۲۳۶ آیه، روایت حفص از عاصم) می‌باشد.

آمار:

• ~۸ تا ۱۱ میلیون قطعه متنی از ۸٬۵۸۹ کتاب اسلامی کلاسیک
• ۶٬۲۳۶ آیه قرآنی (هر آیه = یک قطعه)
• ۴۰ دسته‌بندی — پوشش کامل علوم اسلامی
• دوره: قرن اول تا پانزدهم هجری
• زبان: عربی

به‌طور کامل بر روی یک رایانه شخصی و بدون استفاده از خدمات ابری پردازش شده است. طرح‌واره با Maktabati/openiti-vectors کاملاً سازگار است.

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Technical Details

Parameter	Value
Embedding Model	intfloat/multilingual-e5-base
Vector Dimension	768
Distance Metric	Cosine
Chunk Size	512 tokens
Overlap	50 tokens
Tokenizer	XLM-RoBERTa (via multilingual-e5-base)
Passage Prefix	passage: (indexing)
Query Prefix	query: (search)

Indexed and embedded on a consumer-grade home PC:

Component	Spec
CPU	AMD Ryzen 9 5900X
RAM	32GB DDR4
GPU	AMD Radeon RX 6900 XT (16GB VRAM, ROCm)

No cloud services used; fully reproducible on consumer hardware.

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Dataset Fields

Common Fields (identical with Maktabati/openiti-vectors)

Field	Type	Description
text	string	Original chunk text with full tashkeel
text_norm	string	Normalized text (no tashkeel, unified alef variants)
sha256	string	SHA-256 hash of chunk text (64 chars)
author	string	Arabic author name (e.g. ابن كثير)
title	string	Arabic book title (e.g. تفسير ابن كثير)
death_year	int32	Author death year in AH
language	string	"ar"
page	string	Page reference — books: "V02P045", Quran: "2:255"
char_start	int32	Character offset start within page text
char_end	int32	Character offset end within page text
chunk_no	int32	Chunk index within page
indexed_at	string	ISO 8601 timestamp of indexing

Shamela-specific Fields

Field	Type	Description
source	string	"shamela" or "quran"
book_id	int32	Shamela book ID
page_id	int32	Global page ID
page_num	int32	Page number within volume
part	int32/string	Volume/part number (numeric or Arabic label e.g. "المقدمة")
category_id	int32	Shamela category ID
category_name_ar	string	Category name in Arabic (e.g. كتب السنة)
sequence_num	int32	Reading order sequence number within book
book_type_label	string	Book type (كتاب، رسالة، ...)

Quran-specific Additional Fields

Field	Type	Description
surah_num	int32	Surah number (1–114)
surah_name	string	Surah name in Arabic (e.g. البقرة)
ayah_num	int32	Ayah number within surah
global_id	int32	Sequential Quran verse ID (1–6236)

Note on Quran text_norm: Unlike book entries, text_norm is identical to text for Quran verses — no normalization applied, preserving ة and ى.

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Source Data

• Shamela corpus: AuthenticIlm/Shamela4_Full_DB
• Quran text: Hafs 'an 'Asim (رواية حفص عن عاصم), Majma' al-Malik Fahd (مجمع الملك فهد لطباعة المصحف الشريف), via Shamela4_Full_DB
• Quran verification: Spot-checked Al-Fatiha, Ayat al-Kursi (2:255), Al-Ikhlas, An-Nas — all correct

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Usage

Import into Qdrant

import pyarrow.parquet as pq
from pathlib import Path
from qdrant_client import QdrantClient
from qdrant_client.models import (
    VectorParams, Distance, PointStruct,
    HnswConfigDiff, OptimizersConfigDiff
)

client = QdrantClient("http://localhost:6333")

# Create collection (vectors on disk, HNSW in RAM)
client.create_collection(
    collection_name="shamela",
    vectors_config=VectorParams(size=768, distance=Distance.COSINE, on_disk=True),
    hnsw_config=HnswConfigDiff(m=16, ef_construct=100, on_disk=False),
    optimizers_config=OptimizersConfigDiff(indexing_threshold=0),
    on_disk_payload=True
)

# Import Parquet files
for path in sorted(Path("shamela-vectors").glob("*.parquet")):
    table = pq.read_table(path)
    points = [
        PointStruct(
            id=row["id"],
            vector=row["vector"],
            payload={k: row[k] for k in table.schema.names if k not in ("id", "vector")}
        )
        for row in table.to_pylist()
    ]
    client.upsert(collection_name="shamela", points=points, wait=False)

# Enable HNSW indexing after upload
client.update_collection(
    collection_name="shamela",
    optimizers_config=OptimizersConfigDiff(
        indexing_threshold=1,
        max_optimization_threads=4
    )
)
# Wait until indexed_vectors_count == points_count

Semantic Search

from transformers import AutoTokenizer, AutoModel
import torch
from qdrant_client import QdrantClient

model_name = "intfloat/multilingual-e5-base"
tokenizer  = AutoTokenizer.from_pretrained(model_name)
model      = AutoModel.from_pretrained(model_name)
model.eval()

def embed(text: str) -> list:
    # Important: use "query: " prefix for search queries
    encoded = tokenizer(f"query: {text}", return_tensors="pt",
                        truncation=True, max_length=512)
    with torch.no_grad():
        output = model(**encoded)
    emb = output.last_hidden_state.mean(dim=1)
    return torch.nn.functional.normalize(emb, p=2, dim=1)[0].tolist()

client  = QdrantClient("http://localhost:6333")
vector  = embed("ما حكم الصلاة في الأماكن المغصوبة")
results = client.search(
    collection_name="shamela",
    query_vector=vector,
    limit=10,
    with_payload=True
)

for r in results:
    print(f"Score: {r.score:.3f} | {r.payload['author']} | {r.payload['title']}")
    print(r.payload['text'][:200])
    print()

Filter by Source or Category

from qdrant_client.models import Filter, FieldCondition, MatchValue

# Quran only
results = client.search(
    collection_name="shamela",
    query_vector=vector,
    query_filter=Filter(must=[
        FieldCondition(key="source", match=MatchValue(value="quran"))
    ]),
    limit=10
)

# Hadith books only (كتب السنة)
results = client.search(
    collection_name="shamela",
    query_vector=vector,
    query_filter=Filter(must=[
        FieldCondition(key="category_name_ar",
                       match=MatchValue(value="كتب السنة"))
    ]),
    limit=10
)

# Filter by author
results = client.search(
    collection_name="shamela",
    query_vector=vector,
    query_filter=Filter(must=[
        FieldCondition(key="author", match=MatchValue(value="ابن كثير"))
    ]),
    limit=10
)

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Related Datasets

• Maktabati/openiti-vectors — OpenITI corpus vectors, same schema (Arabic, Persian, Turkish, Urdu)

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License

The texts are from al-Maktaba al-Shamela and subject to their respective licenses. The embeddings and metadata in this dataset are released under CC BY 4.0.

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Citation

@dataset{maktabati_shamela_vectors_2026,
  title     = {Shamela Vector Database},
  author    = {maktabati.ai},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/Maktabati/shamela-vectors},
  note      = {Complete al-Maktaba al-Shamela corpus: 8,589 books + 6,236 Quran verses,
               embedded with intfloat/multilingual-e5-base on consumer hardware}
}