Add comprehensive dataset README

README.md

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+# Vector Database Dataset
+
+Generated embeddings dataset for vector database training and evaluation with multiple format support.
+
+## Dataset Structure
+- **Base dataset**: 1,000,000 samples with embeddings
+- **Query dataset**: 100,000 query samples  
+- **Embedding dimension**: 1024
+
+## Repository Structure
+
+### 📁 parquet/
+Contains parquet files compatible with HuggingFace dataset viewer:
+- `base.parquet` - Main dataset with text and embeddings
+- `queries.parquet` - Query subset for evaluation
+
+### 📁 fvecs/
+Contains .fvecs files for DiskANN compatibility:
+- `base.fvecs` - Base vectors in fvecs format
+- `queries.fvecs` - Query vectors in fvecs format
+
+### 📁 diskann/
+Contains pre-built DiskANN index files:
+- `gt_*.fbin` - Ground truth file
+- `index_*.index` - DiskANN index files
+- Additional index metadata files
+
+## Usage
+
+### Loading with HuggingFace Datasets
+```python
+from datasets import load_dataset
+
+# Load the dataset (uses parquet files automatically)
+dataset = load_dataset("maknee/wikipedia_stella")
+base_data = dataset['base'] 
+query_data = dataset['queries']
+
+# Access embeddings and texts
+import numpy as np
+embeddings = np.array(base_data['embedding'])
+texts = base_data['text']
+```
+
+### Using .fvecs files with DiskANN
+```python
+# Download and use .fvecs files
+from huggingface_hub import hf_hub_download
+
+base_fvecs = hf_hub_download(repo_id="{repo_name}", filename="fvecs/base.fvecs")
+query_fvecs = hf_hub_download(repo_id="{repo_name}", filename="fvecs/queries.fvecs")
+
+# Load with your DiskANN pipeline
+# (Implementation depends on your DiskANN setup)
+```
+
+### Using Pre-built DiskANN Index
+```python
+# Download index files
+from huggingface_hub import hf_hub_download
+import os
+
+# Create local directory for index
+os.makedirs("diskann_index", exist_ok=True)
+
+# Download all index files (adjust filenames as needed)
+index_files = ["gt_100.fbin", "index_64_100_256_disk.index"]  # Example names
+for filename in index_files:
+    hf_hub_download(
+        repo_id="{repo_name}", 
+        filename=f"diskann/{filename}",
+        local_dir="diskann_index"
+    )
+
+# Use with DiskANN search
+# (Implementation depends on your DiskANN setup)
+```
+
+## File Formats
+
+- **Parquet**: Efficient columnar format, compatible with pandas/HuggingFace
+- **fvecs**: Binary format for vector data, used by many vector search libraries  
+- **DiskANN**: Optimized index format for fast similarity search
+
+Generated with the DiskANN embedding generation tool.