Maktabati/openiti-vectors

OpenITI Vector Database — maktabati.ai

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🇬🇧 English

This dataset contains the fully vectorized OpenITI RELEASE 2025-1-9 collection of classical Islamic texts, prepared for semantic search (RAG).

Each entry represents a text chunk from one of 8,943 works in the OpenITI corpus, together with its embedding vector and complete metadata.

Statistics:

• 4,696,703 chunks
• 8,943 works (primary editions only, status=pri from OpenITI TSV)
• 470 Parquet files (~45MB each, zstd-compressed)
• Period: 1st century AH to 14th century AH
• Languages: Arabic (majority), Persian, Turkish, Urdu

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🇸🇦 العربية

يحتوي هذا المجموعة البيانات على مجموعة OpenITI RELEASE 2025-1-9 المُرقَّمة بالكامل من النصوص الإسلامية الكلاسيكية، مُعدَّة للبحث الدلالي (RAG).

يمثّل كل إدخال مقطعًا نصيًا من أحد 8,943 مصنَّفًا في مجموعة OpenITI، مع متجه التضمين والبيانات الوصفية الكاملة.

الإحصاءات:

• 4,696,703 مقطع نصي
• 8,943 مصنَّف (الطبعات الأولية فقط)
• نصوص من القرن الأول الهجري حتى القرن الرابع عشر الهجري
• اللغات: العربية (الأغلب)، الفارسية، التركية، الأردية

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🇩🇪 Deutsch

Dieses Dataset enthält die vollständig vektorisierte OpenITI RELEASE 2025-1-9 Sammlung klassischer islamischer Texte, aufbereitet für semantische Suche (RAG).

Jeder Eintrag repräsentiert einen Text-Chunk aus einem der 8.943 Werke der OpenITI-Sammlung, zusammen mit seinem Embedding-Vektor und vollständigen Metadaten.

Statistiken:

• 4.696.703 Chunks
• 8.943 Werke (nur Primäreditionen)
• Zeitraum: 1. bis 14. Jahrhundert AH
• Sprachen: Arabisch (Hauptteil), Persisch, Türkisch, Urdu

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🇹🇷 Türkçe

Bu veri kümesi, semantik arama (RAG) için hazırlanmış OpenITI RELEASE 2025-1-9 klasik İslam metinleri koleksiyonunun tam vektörleştirilmiş halini içermektedir.

Her giriş, OpenITI koleksiyonundaki 8.943 eserden birinin metin parçasını, gömme vektörü ve eksiksiz meta verilerle birlikte temsil etmektedir.

İstatistikler:

• 4.696.703 metin parçası
• 8.943 eser (yalnızca birincil baskılar)
• Dönem: 1. ile 14. yüzyıl Hicri
• Diller: Arapça (çoğunluk), Farsça, Türkçe, Urduca

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🇮🇷 فارسی

این مجموعه داده شامل مجموعه متون کلاسیک اسلامی OpenITI RELEASE 2025-1-9 است که به صورت کامل برداری شده و برای جستجوی معنایی (RAG) آماده شده است.

هر ورودی نشان‌دهنده یک قطعه متنی از یکی از ۸٬۹۴۳ اثر در مجموعه OpenITI، همراه با بردار جاسازی و فراداده‌های کامل است.

آمار:

• ۴٬۶۹۶٬۷۰۳ قطعه متنی
• ۸٬۹۴۳ اثر (فقط نسخه‌های اصلی)
• دوره: قرن اول تا چهاردهم هجری
• زبان‌ها: عربی (اکثریت)، فارسی، ترکی، اردو

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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
Passage Prefix	passage: (indexing)
Query Prefix	query: (search)

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Dataset Fields

Field	Type	Description
id	string	Deterministic SHA256-based ID
vector	float32[768]	Embedding vector
text	string	Original chunk text
text_norm	string	Normalized text (no tashkeel, unified alef variants)
sha256	string	SHA256 hash of chunk text
autor	string	Arabic author name (e.g. الطبري)
titel	string	Arabic book title (e.g. تاريخ الطبري)
todesjahr	int32	Author death year in AH (e.g. 310)
sprache	string	Language code (ar, fa, tr, ur)
seite	string	Last OpenITI page marker before chunk (e.g. PageV01P023)
char_start	int64	Character position in source text (start)
char_end	int64	Character position in source text (end)
datei_name	string	OpenITI filename (e.g. 0310Tabari.TarikhIslam.Shamela-ara1)
chunk_nr	int32	Chunk number within file
indexiert	string	ISO timestamp of indexing

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Source Data

• OpenITI RELEASE 2025-1-9: https://github.com/OpenITI/RELEASE
• Metadata from official OpenITI_metadata_2025-1-9.tsv (author names, titles, edition status)
• Primary editions only (status=pri from TSV)
• When multiple primary editions exist: longest by char_length

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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
client.create_collection(
    collection_name="openiti",
    vectors_config=VectorParams(size=768, distance=Distance.COSINE, on_disk=True),
    hnsw_config=HnswConfigDiff(m=0),  # disable HNSW during upload
    optimizers_config=OptimizersConfigDiff(indexing_threshold=0)
)

# Import Parquet files
for path in sorted(Path("openiti-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="openiti", points=points, wait=False)

# Enable HNSW after upload
client.update_collection(
    collection_name="openiti",
    hnsw_config=HnswConfigDiff(m=16, ef_construct=100, on_disk=False),
    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 قاله ابن كثير عن يأجوج ومأجوج")
results = client.search(
    collection_name="openiti",
    query_vector=vector,
    limit=10,
    with_payload=True
)

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

Filter by Author

from qdrant_client.models import Filter, FieldCondition, MatchValue

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

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License

The texts are from the OpenITI project 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_openiti_2025,
  title     = {OpenITI Vector Database},
  author    = {maktabati.ai},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/Maktabati/openiti-vectors},
  note      = {Based on OpenITI RELEASE 2025-1-9, 4,696,703 chunks, indexed 2026}
}

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Contact

Created as part of the maktabati.ai project — semantic search in classical Islamic texts.