Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- feature-extraction
+- text-retrieval
+- question-answering
+language:
+- en
+tags:
+- wikipedia
+- embeddings
+- faiss
+- vector-database
+- rag
+- ivf
+- pq
+- gpu
+size_categories:
+- 10M<n<100M
+---
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: embeddings
+    dtype: float32
+    shape: [384]
+  configs:
+  - config_name: default
+    data_files: "*.parquet"
+---
+
+# Wikipedia IVF-OPQ-PQ Vector Database (GPU-Optimized)
+
+A high-performance, GPU-accelerated FAISS vector database built from Wikipedia articles with pre-computed embeddings. This dataset contains approximately 35 million Wikipedia articles with 384-dimensional embeddings using the `all-MiniLM-L6-v2` model.
+
+## Dataset Overview
+
+This vector database uses advanced compression techniques (IVF + OPQ + PQ) to provide fast similarity search over Wikipedia content while maintaining high recall. The database is optimized for Retrieval Augmented Generation (RAG) applications and large-scale semantic search.
+
+**Key Features:**
+- **GPU-accelerated FAISS index** with IVF, OPQ, and Product Quantization
+- **SQLite text storage** with aligned vector IDs
+- **Memory-efficient** compression (~64 bytes per vector)
+
+## Dataset Structure
+wikipedia_vector_index_DB/
+├── index.faiss              # Main FAISS index (CPU-serialized)
+├── meta.json                # Index metadata and parameters
+├── docs.sqlite              # Text storage (rowid = vector id)
+├── docs.sqlite-wal          # SQLite WAL file (if present)
+└── docs.sqlite-shm          # SQLite shared memory (if present)
+
+### File Descriptions
+
+- **`index.faiss`**: Complete FAISS index containing trained OPQ matrices, IVF centroids, PQ codebooks, and compressed vector codes
+- **`meta.json`**: Checkpoint metadata including offset, ntotal, dimensions, and compression parameters  
+- **`docs.sqlite`**: SQLite database with schema `docs(id INTEGER PRIMARY KEY, text TEXT)` where `id` matches FAISS vector IDs
+- **`*.parquet`**: Original embedding data in Parquet format for verification and rebuilding
+
+## Technical Specifications
+
+| Parameter | Value | Description |
+|-----------|-------|-------------|
+| **Vectors** | ~35M | Total number of Wikipedia articles |
+| **Dimensions** | 384 | Embedding dimensionality (all-MiniLM-L6-v2) |
+| **Index Type** | IVF-OPQ-PQ | Inverted File + Optimized Product Quantization |
+| **Compression** | ~64 bytes/vector | Memory-efficient storage |
+| **nlist** | 131k-262k | Number of IVF clusters |
+| **OPQ** | 64 subspaces | Optimized rotation matrix |
+| **PQ** | 64×8 bits | Product quantization parameters |
+
+## Usage
+
+### Quick Start
+```python
+from huggingface_hub import snapshot_download
+import faiss
+import sqlite3
+import json
+
+# Download the complete vector database
+dataset_path = snapshot_download(
+    repo_id="your-username/wikipedia-vector-db",
+    repo_type="dataset",
+    cache_dir="./data"
+)
+
+# Load FAISS index
+index = faiss.read_index(f"{dataset_path}/index.faiss")
+
+# Load metadata
+with open(f"{dataset_path}/meta.json", "r") as f:
+    meta = json.load(f)
+
+# Connect to text database
+conn = sqlite3.connect(f"{dataset_path}/docs.sqlite")
+
+print(f"Loaded index with {index.ntotal:,} vectors")
+print(f"Index dimension: {index.d}")
+
+###GPU Accelerated
+import faiss
+
+# Move index to GPU for faster queries
+res = faiss.StandardGpuResources()
+gpu_index = faiss.index_cpu_to_gpu(res, 0, index)
+
+# Set search parameters
+gpu_index.nprobe = 128  # Higher = better recall, slower search
+
+# Perform similarity search
+query_vector = get_query_embedding("your search query")  # Shape: (1, 384)
+distances, indices = gpu_index.search(query_vector, k=10)
+
+# Retrieve corresponding text
+cursor = conn.cursor()
+for idx in indices[0]:
+    result = cursor.execute("SELECT text FROM docs WHERE id = ?", (int(idx),)).fetchone()
+    if result:
+        print(f"ID {idx}: {result[0][:200]}...")
+
+Original Dataset
+This vector database is built from maloyan/wikipedia-22-12-en-embeddings-all-MiniLM-L6-v2, which contains pre-computed embeddings of Wikipedia articles using the sentence-transformers/all-MiniLM-L6-v2 model.