README.md

---
license: mit
task_categories:
- feature-extraction
- sentence-similarity
language:
- en
tags:
- embeddings
- vector-search
- similarity-search
- diskann
- dbpedia
- openai
size_categories:
- 1M<n<10M
---

# DBpedia OpenAI 1M Dataset

A comprehensive vector database resource containing 1,000,000 DBpedia entity descriptions with pre-computed OpenAI text-embedding-ada-002 embeddings (1536-D). This dataset is optimized for large-scale similarity search, retrieval tasks, and distributed vector database deployments.

## Dataset Overview

- **Size**: 1,000,000 base vectors + 10,000 query vectors
- **Embedding Model**: OpenAI text-embedding-ada-002
- **Dimensions**: 1536
- **Source**: [KShivendu/dbpedia-entities-openai-1M](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M)
- **Format**: Parquet, FBIN, DiskANN indices
- **License**: MIT

## Dataset Structure

Each record contains:

| Field | Type | Description |
|-------|------|-------------|
| **id** | int64 | Unique entity identifier |
| **text** | string | DBpedia entity description |
| **embedding** | float32[1536] | Pre-computed OpenAI embedding vector |

## Directory Structure

```
dbpedia_openai_1m/
├── parquet/                    # Raw embeddings in Parquet format
│   ├── base.parquet           # 1M base vectors (8.6 GB)
│   └── queries.parquet        # 10K query vectors (88 MB)
├── fbin/                       # Binary vector format for DiskANN
│   ├── base.fbin              # 1M base vectors (5.8 GB)
│   └── queries.fbin           # 10K query vectors (59 MB)
└── diskann/                    # DiskANN indices and shards
    ├── index_64_100_384_disk.index   # Base index (R=64, L=100, PQ=384)
    ├── shard_3/               # 3-shard configuration (27 files)
    ├── shard_5/               # 5-shard configuration (45 files)
    ├── shard_7/               # 7-shard configuration (63 files)
    └── shard_10/              # 10-shard configuration (90 files)
```

## DiskANN Index Configuration

All indices are built with the following parameters:

- **Graph Degree (R)**: 64
- **Search List Size (L)**: 100
- **PQ Compression**: 384 bytes (0.25 ratio)
- **Distance Metric**: L2 (Euclidean)
- **Build RAM Budget**: 100 GB
- **Search RAM Budget**: 0.358 GB per query

### Shard Configurations

Each shard configuration includes:
- **Data shards**: `.fbin` files with partitioned vectors
- **Index files**: `_disk.index` DiskANN graph indices
- **PQ files**: `_pq_compressed.bin` and `_pq_pivots.bin` for compression
- **MinIO format**: `_512_none.indices` and `_base_none.vectors` for distributed deployment

| Configuration | Shards | Vectors/Shard | Use Case |
|---------------|--------|---------------|----------|
| shard_3 | 3 | ~333K | Small clusters |
| shard_5 | 5 | ~200K | Medium clusters |
| shard_7 | 7 | ~143K | Large clusters |
| shard_10 | 10 | ~100K | Distributed systems |

## Usage

### Load with Hugging Face Datasets

```python
from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("maknee/dbpedia_openai_1m")

# Access embeddings and text
for item in dataset['train']:
    text = item['text']
    embedding = item['embedding']  # 1536-D vector
    print(f"Text: {text[:100]}...")
    print(f"Embedding shape: {len(embedding)}")
```

### Load Parquet Directly

```python
import pandas as pd

# Load base vectors
base_df = pd.read_parquet("hf://datasets/maknee/dbpedia_openai_1m/parquet/base.parquet")
print(f"Loaded {len(base_df)} vectors with {len(base_df['embedding'].iloc[0])} dimensions")

# Load queries
queries_df = pd.read_parquet("hf://datasets/maknee/dbpedia_openai_1m/parquet/queries.parquet")
print(f"Loaded {len(queries_df)} query vectors")
```

### Use with DiskANN

```python
from huggingface_hub import hf_hub_download

# Download base index
index_path = hf_hub_download(
    repo_id="maknee/dbpedia_openai_1m",
    filename="diskann/index_64_100_384_disk.index",
    repo_type="dataset"
)

# Use with DiskANN library for similarity search
# See: https://github.com/microsoft/DiskANN
```

### Distributed Deployment

For distributed vector search systems:

1. **Download a shard configuration** (e.g., 5 shards):
```python
from huggingface_hub import snapshot_download

# Download all shard_5 files
local_dir = snapshot_download(
    repo_id="maknee/dbpedia_openai_1m",
    repo_type="dataset",
    allow_patterns="diskann/shard_5/*"
)
```

2. **Deploy shards across nodes**: Each shard contains:
   - Data: `base_base.shard{N}.fbin`
   - Index: `base_64_100_384.shard{N}_disk.index`
   - MinIO files: `*_512_none.indices` and `*_base_none.vectors`

3. **Query in parallel**: Distribute queries across all shard nodes and merge results.

## Use Cases

- **Semantic Search**: Find similar DBpedia entities by text similarity
- **Knowledge Base Retrieval**: Build QA systems over structured knowledge
- **Entity Linking**: Connect text mentions to DBpedia entities
- **Recommendation Systems**: Suggest related entities based on embeddings
- **Vector Database Benchmarking**: Test large-scale similarity search systems
- **Distributed Systems Research**: Study sharding and parallel search strategies

## Dataset Statistics

- **Total Vectors**: 1,000,000 base + 10,000 queries
- **Embedding Dimensions**: 1536
- **Total Dataset Size**: ~35 GB (all formats)
- **Average Text Length**: ~200 characters
- **Coverage**: Diverse DBpedia entity types (people, places, organizations, concepts)

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{dbpedia_openai_1m,
  author = {maknee},
  title = {DBpedia OpenAI 1M: Pre-embedded DBpedia Entities},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/maknee/dbpedia_openai_1m}
}
```

Original DBpedia embeddings from:
```bibtex
@dataset{kshivendu_dbpedia,
  author = {KShivendu},
  title = {DBpedia Entities OpenAI 1M},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M}
}
```

## License

This dataset is released under the MIT License. The original embeddings are from OpenAI's text-embedding-ada-002 model.

## Acknowledgments

- **Original Dataset**: [KShivendu/dbpedia-entities-openai-1M](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M)
- **Embedding Model**: OpenAI text-embedding-ada-002
- **Index Format**: Microsoft DiskANN
- **Source Knowledge Base**: DBpedia

## Related Datasets

- [maknee/wikipedia_qwen_4b](https://huggingface.co/datasets/maknee/wikipedia_qwen_4b) - Wikipedia with Qwen embeddings (2560-D)
- [maknee/wikipedia_qwen_8b](https://huggingface.co/datasets/maknee/wikipedia_qwen_8b) - Wikipedia with Qwen-8B embeddings

## Contact

For questions or issues, please open an issue on the [dataset repository](https://huggingface.co/datasets/maknee/dbpedia_openai_1m/discussions).