Add comprehensive README with usage examples

README.md

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+---
+license: mit
+task_categories:
+  - question-answering
+  - text-generation
+language:
+  - as
+  - bn
+  - en
+  - gu
+  - hi
+  - ks
+  - mai
+  - ml
+  - mni
+  - mr
+  - ne
+  - or
+  - pa
+  - sat
+  - ta
+  - te
+multilinguality: multilingual
+size_categories:
+  - 1M<n<10M
+tags:
+  - indian-languages
+  - multilingual
+  - indic
+  - qa-dataset
+  - rag
+  - wikipedia
+pretty_name: "Multilingual Indic RAG Suite"
+configs:
+  - config_name: as
+    data_files: data/as_data.parquet
+  - config_name: bn
+    data_files: data/bn_data.parquet
+  - config_name: en
+    data_files: data/en_data.parquet
+  - config_name: gu
+    data_files: data/gu_data.parquet
+  - config_name: hi
+    data_files: data/hi_data.parquet
+  - config_name: ks
+    data_files: data/ks_data.parquet
+  - config_name: mai
+    data_files: data/mai_data.parquet
+  - config_name: ml
+    data_files: data/ml_data.parquet
+  - config_name: mni
+    data_files: data/mni_data.parquet
+  - config_name: mr
+    data_files: data/mr_data.parquet
+  - config_name: ne
+    data_files: data/ne_data.parquet
+  - config_name: or
+    data_files: data/or_data.parquet
+  - config_name: pa
+    data_files: data/pa_data.parquet
+  - config_name: sat
+    data_files: data/sat_data.parquet
+  - config_name: ta
+    data_files: data/ta_data.parquet
+  - config_name: te
+    data_files: data/te_data.parquet
+---
+
+# 🌏 Multilingual Indic RAG Suite
+
+A comprehensive multilingual question-answering dataset covering **16 Indian languages** with **20,125,799 total samples**, designed for RAG (Retrieval-Augmented Generation) applications and multilingual NLP research.
+
+## 🚀 Quick Start
+
+```python
+from datasets import load_dataset
+
+# Load specific language (recommended)
+dataset = load_dataset("ai4bharat/Indic-Rag-Suite", "as")
+train_data = dataset['train']
+
+print(f"Loaded {len(train_data)} samples")
+
+# Access examples
+for example in train_data[:2]:
+    print(f"Q: {example['question']}")
+    print(f"A: {example['answer']}")
+    print("---")
+```
+
+## 📊 Dataset Overview
+
+### Available Languages (16 total)
+
+| Code | Language | Load Command |
+|------|----------|--------------|
+| `as` | Assamese | `load_dataset('ai4bharat/Indic-Rag-Suite', 'as')` |
+| `bn` | Bengali | `load_dataset('ai4bharat/Indic-Rag-Suite', 'bn')` |
+| `en` | English | `load_dataset('ai4bharat/Indic-Rag-Suite', 'en')` |
+| `gu` | Gujarati | `load_dataset('ai4bharat/Indic-Rag-Suite', 'gu')` |
+| `hi` | Hindi | `load_dataset('ai4bharat/Indic-Rag-Suite', 'hi')` |
+| `ks` | Kashmiri | `load_dataset('ai4bharat/Indic-Rag-Suite', 'ks')` |
+| `mai` | Maithili | `load_dataset('ai4bharat/Indic-Rag-Suite', 'mai')` |
+| `ml` | Malayalam | `load_dataset('ai4bharat/Indic-Rag-Suite', 'ml')` |
+| `mni` | Manipuri | `load_dataset('ai4bharat/Indic-Rag-Suite', 'mni')` |
+| `mr` | Marathi | `load_dataset('ai4bharat/Indic-Rag-Suite', 'mr')` |
+| `ne` | Nepali | `load_dataset('ai4bharat/Indic-Rag-Suite', 'ne')` |
+| `or` | Odia | `load_dataset('ai4bharat/Indic-Rag-Suite', 'or')` |
+| `pa` | Punjabi | `load_dataset('ai4bharat/Indic-Rag-Suite', 'pa')` |
+| `sat` | Santali | `load_dataset('ai4bharat/Indic-Rag-Suite', 'sat')` |
+| `ta` | Tamil | `load_dataset('ai4bharat/Indic-Rag-Suite', 'ta')` |
+| `te` | Telugu | `load_dataset('ai4bharat/Indic-Rag-Suite', 'te')` |
+
+
+### Dataset Statistics
+- **Total Samples**: 20,125,799
+- **Languages**: 16
+- **Source**: Wikipedia + Llama 3.3 70B
+- **Task**: Question Answering with Reasoning
+
+## 📋 Data Structure
+
+Each sample contains:
+- `question`: The question text
+- `answer`: The answer text
+- `reasoning`: Detailed reasoning/explanation
+- `paragraph`: Source Wikipedia paragraph
+- `title`: Wikipedia article title
+- `wiki_id`: Wikipedia article ID
+- `url`: Wikipedia URL
+- `language`: Language code
+- `text`: Combined question+answer+reasoning for RAG
+
+## 💡 Usage Examples
+
+### Load Single Language
+```python
+from datasets import load_dataset
+
+# Most efficient - load only what you need
+hindi_dataset = load_dataset("ai4bharat/Indic-Rag-Suite", "hi")
+train_data = hindi_dataset['train']
+
+# Convert to pandas for analysis
+import pandas as pd
+df = train_data.to_pandas()
+print(f"Dataset shape: {df.shape}")
+```
+
+### Load Multiple Languages
+```python
+languages = ['hi', 'bn', 'ta', 'en']
+datasets = {}
+
+for lang in languages:
+    datasets[lang] = load_dataset("ai4bharat/Indic-Rag-Suite", lang)
+    print(f"{lang}: {len(datasets[lang]['train'])}")
+```
+
+---
+
+**Dataset URL**: https://huggingface.co/datasets/ai4bharat/Indic-Rag-Suite  
+**Total Samples**: 20,125,799  
+**Languages**: 16  
+**Last Updated**: 2025-06-07