---
license: mit
language:
- ur
tags:
- rag
- urdu
- question-answering
- vector-database
- text-corpus
size_category: n<1K
---

# 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:

```python
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.")