---
task_categories:
- text-retrieval
- sentence-similarity
language:
- en
tags:
- embeddings
- vector-database
- benchmark
---

# GAS Indexing Artifacts

## Dataset Description

This dataset contains pre-computed deterministic centroids and associated geometric metadata generated using our GAS (Geometry-Aware Selection) algorithm. 
These artifacts are designed to benchmark Approximate Nearest Neighbor (ANN) search performance in privacy-preserving or dynamic vector database environments.

### Purpose

To serve as a standardized benchmark resource for evaluating the efficiency and recall of vector databases implementing the GAS architecture. 
It is specifically designed for integration with VectorDBBench.

### Dataset Summary

- **Source Data**: Wikipedia (Public Dataset)
- **Embedding Model**: [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m)

## Dataset Structure

For each embedding model, the directory contains two key file:

| Data | Description |
|-------|-------------|
| `centroids.npy` | centroids as followed IVF |
| `tree_info.pkl` | tree metadata with parent and leaf info |

## Data Fields

### Centroids: `centroids.npy`

- **Purpose**: Finding the nearest clusters for IVF (Inverted File Index)
- **Type**: NumPy array (`np.ndarray`)
- **Shape**: `[32768, 768]`
- **Description**: 768-dimensional vectors representing 32,768 cluster centroids
- **Normalization**: L2-normalized (unit norm)
- **Format**: float32

### Tree Metadata: `tree_info.pkl`

- **Purpose**: Finding virtual clusters following hierarchical tree structure for efficient GAS search
- **Type**: Python dictionary (pickle)
- **Keys**:
  - `node_parents`: Dictionary mapping each node ID to its parent node ID
    - Format: `{node_id: parent_node_id, ...}`
    - Contains parent-child relationships for all nodes in the tree
  
  - `leaf_ids`: List of leaf node IDs
    - Format: `[leaf_id_1, leaf_id_2, ..., leaf_id_32768]`
    - Total 32,768 leaf nodes (corresponding to 32,768 centroids)
  
  - `leaf_to_centroid_idx`: Mapping from leaf node IDs to centroid indices in `centroids.npy`
    - Format: `{leaf_node_id: centroid_index, ...}`
    - Maps each leaf node to its corresponding row index in `centroids.npy`
    - Important: Leaf IDs in `leaf_ids` are ordered sequentially, so the i-th leaf corresponds to the i-th centroid


## Dataset Creation

### Source Data

Source dataset is a large public dataset, Wikipedia: [mixedbread-ai/wikipedia-data-en-2023-11](https://huggingface.co/datasets/mixedbread-ai/wikipedia-data-en-2023-11).

### Preprocessing

1. Create Centroids by GAS approach:

   Description TBD

2. Chunking: For texts exceeding 2048 tokens:

    - Split into chunks with ~100 token overlap
    - Embedded each chunk separately
    - Averaged chunk embeddings for final representation
   
3. Normalization: All embeddings are L2-normalized

### Embedding Generation

- Model: google/embeddinggemma-300m
- Dimension: 768
- Max Token Length: 2048
- Normalization: L2-normalized

## Usage

```python
import wget

def download_centroids(embedding_model: str, dataset_dir: str) -> None:
    """Download pre-computed centroids and tree info for GAS."""
    dataset_link = "https://huggingface.co/datasets/cryptolab-playground/gas-centroids/resolve/main/embeddinggemma-300m"
    wget.download(f"{dataset_link}/centroids.npy", out="centroids.npy")
    wget.download(f"{dataset_link}/tree_info.pkl", out="tree_info.pkl")
```

## License

Apache 2.0

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{gas-centroids,
  author = {CryptoLab, Inc.},
  title = {GAS Centroids},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/cryptolab-playground/gas-centroids}
}
```

### Source Dataset Citation

```bibtex
@dataset{wikipedia_data_en_2023_11,
  author = {mixedbread-ai},
  title = {Wikipedia Data EN 2023 11},
  year = {2023},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/mixedbread-ai/wikipedia-data-en-2023-11}
}
```

### Embedding Model Citation

```bibtex
@misc{embeddinggemma,
  title={Embedding Gemma},
  author={Google},
  year={2024},
  url={https://huggingface.co/google/embeddinggemma-300m}
}
```

### Acknowledgments

- Original dataset: mixedbread-ai/wikipedia-data-en-2023-11
- Embedding model: google/embeddinggemma-300m
- Benchmark framework: VectorDBBench