Floressek/wiki-1m-qdrant-snapshot

Wikipedia 1M — Embedding Snapshot (Qdrant, 768D, GTE Multilingual Base)

This dataset contains a 7GB Qdrant snapshot with 1,000,000 Polish Wikipedia passages, embedded using:

• Model: Alibaba-NLP/gte-multilingual-base
• Embedding dimension: 768
• Distance metric: cosine
• Index type: HNSW (M=32, ef_construct=256, on-disk enabled)
• Chunking strategy: semantic, max chunk size 512, overlap 128
• Payloads: include passage text + metadata

The snapshot can be restored directly using the Qdrant client.

Because the content originates from Wikipedia, the dataset is distributed under
CC-BY-SA-4.0, in accordance with the original CC-BY-SA-3.0 license.

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Dataset Details

Source

The dataset consists of the first 1M processed Wikipedia passages, chunked and embedded via the RAGx pipeline:

• Chunker model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
• Chunker strategy: semantic segmentation, section-aware
• Embedding model: Alibaba-NLP/gte-multilingual-base
• Query prefix: query:
• Passage prefix: passage:
• Max seq length: 512

Qdrant Configuration

• Collection name: ragx_documents_1M_main_sample
• Vectors: 1,000,000
• Dimensionality: 768
• Distance: cosine
• Index: HNSW, on-disk enabled
• Search EF: 256

Contents

The snapshot contains:

• vectors (1M embeddings)
• payloads (raw chunk text, document metadata)
• Qdrant index structure (HNSW, WAL, snapshot)

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Uses

Direct Use

• Retrieval-Augmented Generation (RAG)
• Hybrid retrievers with cross-encoders (e.g., Jina Reranker)
• Multi-hop retrieval
• Semantic search benchmarking
• Qdrant index bootstrapping
• Testing LLM-based chain-of-verification systems (CoVe)

Out-of-Scope Use

• Reconstructing original Wikipedia pages from embeddings
• Tasks requiring full textual content without attribution

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Loading the Snapshot

Python

from huggingface_hub import hf_hub_download
from qdrant_client import QdrantClient

path = hf_hub_download(
    repo_id="floressek/wiki-1m-qdrant-snapshot",
    filename="wiki_1m_qdrant.snapshot",
    repo_type="dataset"
)

client = QdrantClient(path=path, storage="snapshot")

Qdrant CLI

qdrant snapshot recover wiki_1m_qdrant.snapshot

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Licensing

Wikipedia text is licensed under CC-BY-SA 3.0, and therefore all derivative works — including embeddings — must follow a share-alike license.

This dataset is released under CC-BY-SA 4.0.

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Citation

If you use this dataset, please cite:

Wikipedia:

Wikipedia contributors. (2024). Wikipedia, The Free Encyclopedia.

GTE Multilingual Base:

Alibaba-NLP/gte-multilingual-base

Qdrant:

Qdrant: Scalable Vector Search Engine. https://qdrant.tech

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Dataset Card Contact

Maintainer: Floressek Questions / issues: open an Issue in this repo.