fatymahaly/urdu_rag_dataset.csv

Dataset Card for Urdu RAG Knowledge Base

Dataset Overview

This dataset is designed specifically to bootstrap and evaluate Retrieval-Augmented Generation (RAG) applications, search systems, and semantic retrieval pipelines using the Urdu language. It contains 185 clean, structured, and informative text chunks covering a wide array of domains.

• Language: Urdu (ur)
• Script: Nastaliq / Arabic script (Unicode UTF-8)
• Total Rows: 185 chunks
• Format: CSV (id, title, content)
• Primary Use Case: Vector embedding ingestion, semantic search, retrieval testing, and knowledge-base seeding for Urdu LLM applications.

Dataset Structure

The dataset follows a tabular CSV schema with three primary columns:

Column Name	Data Type	Description
id	Integer	Unique identifier for each text chunk (1 to 185).
title	String (Urdu)	The subject heading or topic name of the text chunk.
content	String (Urdu)	The core informational paragraph or passage used for vector embedding and context retrieval.

Content Domains Covered

The 185 entries span a balanced mix of topics to ensure diverse semantic representation in vector databases:

1. Geography & Provinces: Detailed entries on Punjab, Sindh, Khyber Pakhtunkhwa, Balochistan, Gilgit-Baltistan, Azad Kashmir, and major cities (Lahore, Karachi, Peshawar, Quetta, Islamabad, Multan, Faisalabad, etc.).
2. History & National Heritage: Landmark historical events, archaeological sites (Mohenjo-daro, Harappa, Taxila, Rohtas Fort), and the Pakistan Movement.
3. Culture, Art & Literature: Prominent Urdu poets and writers (Mir, Ghalib, Iqbal, Faiz, Manto), traditional attire, regional festivals, music (Sufi/Qawwali), and crafts.
4. Science, Technology & RAG Development: Modern technical articles covering IT outsourcing, AI, Natural Language Processing (NLP), vector embeddings, RAG architectures, and programming.
5. Economy, Agriculture & Industry: Key economic drivers, textile sectors, exports (rice, mangoes, sports goods, surgical instruments), CPEC, and energy projects.
6. Sports & Achievements: National sports, cricket world cups, squash legends (Jahangir Khan, Jansher Khan), and athletics (Arshad Nadeem).

How to Load and Use

You can easily ingest this dataset into your Python-based RAG pipeline (using Pandas, LangChain, or LlamaIndex) with the following snippet:

import pandas as pd

# Load the dataset
df = pd.read_csv("urdu_rag_dataset.csv")

# Preview the first few rows
print(df.head())

# Iterate through chunks to create documents for a vector store
from langchain.schema import Document

documents = [
    Document(page_content=row['content'], metadata={"id": row['id'], "title": row['title']})
    for _, row in df.iterrows()
]

print(f"Loaded {len(documents)} documents for RAG vectorization.")