maknee/dbpedia_openai_1m

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
• 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

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

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

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):

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:

@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:

@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
• Embedding Model: OpenAI text-embedding-ada-002
• Index Format: Microsoft DiskANN
• Source Knowledge Base: DBpedia

Related Datasets

• maknee/wikipedia_qwen_4b - Wikipedia with Qwen embeddings (2560-D)
• maknee/wikipedia_qwen_8b - Wikipedia with Qwen-8B embeddings

Contact

For questions or issues, please open an issue on the dataset repository.