README.md

# abstracts_embeddings - Sharded DiskANN Indices

Pre-built DiskANN indices sharded for distributed vector search.

## Dataset Info
- **Source**: [colonelwatch/abstracts-embeddings](https://huggingface.co/datasets/colonelwatch/abstracts-embeddings)
- **Vectors**: 10,000,000
- **Dimensions**: 1024 (float32)
- **Queries**: 10,000

## DiskANN Parameters
- **R** (graph degree): 64
- **L** (build beam width): 100
- **Distance**: L2

## Shard Configurations
- **shard_10**: 10 shards x 1,000,000 vectors
- **shard_3**: 3 shards x 3,333,333 vectors
- **shard_5**: 5 shards x 2,000,000 vectors
- **shard_7**: 7 shards x 1,428,571 vectors

## File Structure
```
fbin/
  base.fbin          # Base vectors (10,000,000 x 1024 x float32)
  queries.fbin       # Query vectors (10K x 1024 x float32)
diskann/
  shard_N/           # N-shard configuration
    *_disk.index                    # DiskANN disk index
    *_disk.index_512_none.indices   # MinIO graph indices
    *_disk.index_base_none.vectors  # MinIO vector data
    *_base.shardX.fbin              # Shard base data
```

## Usage

### Download with huggingface_hub
```python
from huggingface_hub import hf_hub_download

# Download a specific shard configuration
index = hf_hub_download(
    repo_id="makneeeee/abstracts_embeddings",
    filename="diskann/shard_3/abstracts_embeddings_64_100_256.shard0_disk.index",
    repo_type="dataset"
)
```

### Download with git-lfs
```bash
git lfs install
git clone https://huggingface.co/datasets/makneeeee/abstracts_embeddings
```

## License
Same as source dataset.