Docs: Initial plans

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README.md

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+# WikiVerse
+
+Multi-model embedding dataset built on [HuggingFace FineWiki](https://huggingface.co/datasets/HuggingFaceFW/finewiki), designed for approximate nearest neighbor (ANN) search benchmarking with [USearch](https://github.com/unum-cloud/usearch) and other vector search engines.
+
+The same Wikipedia corpus — chunked, cleaned, and enriched with graph metadata — is embedded by multiple models spanning dense encoders, decoder-based LLMs, and late-interaction (ColBERT-style) architectures.
+Each model's embeddings ship with precomputed ground-truth k-nearest neighbors, enabling reproducible recall and throughput benchmarks without re-running expensive exact search.
+
+## Why WikiVerse?
+
+Existing ANN benchmarks suffer from three gaps:
+
+1. __Stale descriptors.__
+   The most popular benchmarks (SIFT-1B, Deep-1B, GloVe) use features from 2014-2021 — image descriptors and word vectors, not modern text embeddings.
+2. __Single-model datasets.__
+   Each benchmark is produced by one model.
+   You cannot compare how the _same_ retrieval engine handles different vector distributions without re-embedding.
+3. __No decoder embeddings.__
+   State-of-the-art embedding models (GTE-Qwen, Llama-Embed-Nemotron, Qwen3-Embedding) are decoder-based LLMs, yet no ANN benchmark uses their outputs.
+
+WikiVerse fixes all three: one corpus, multiple models, modern architectures, with graph-structured metadata for filtered search.
+
+## Source Corpus
+
+[HuggingFaceFW/finewiki](https://huggingface.co/datasets/HuggingFaceFW/finewiki) — August 2025 snapshot, 325 languages, 61.5M articles.
+
+FineWiki is extracted from Wikimedia's __Enterprise HTML dumps__ (not raw wikitext), so templates are fully rendered by MediaWiki's own engine.
+This avoids the well-known content loss that plagues wikitext-based parsers, as `mwparserfromhell` cannot expand templates.
+Section headings, tables, math, and lists are preserved as Markdown.
+Bot-generated stubs, disambiguation pages, and cross-language leakage are filtered out.
+
+### Text processing
+
+No chunking is used.
+Short-content models only process the abstract.
+Long-context models are prioritized and receive the whole document in the original form.
+
+### Scale
+
+| Scope                     | Articles | Parquet, GB | Avg Bytes/Article |
+| :------------------------ | -------: | ----------: | ----------------: |
+| English                   |     6.6M |          38 |             5,700 |
+| Top 5: EN, DE, FR, ES, RU |    15.7M |          86 |             5,460 |
+| Top 10 by text volume¹    |    22.9M |         120 |             5,250 |
+| Top 20                    |    41.6M |         149 |             3,580 |
+| All 325 languages         |    61.6M |        ~170 |             2,740 |
+
+¹ EN, DE, FR, ES, RU, IT, JA, ZH, PL, UK — excluding bot-generated wikis (Cebuano, Swedish, Waray, Egyptian Arabic) which inflate article counts with minimal text.
+
+Parquet weight includes both `text` and `wikitext` columns; pure text is roughly half.
+Average bytes/article drops at wider scope because smaller wikis are dominated by stubs.
+
+## Embedding Models
+
+Each model embeds the same article corpus independently.
+No chunking is applied — short-context models see truncated articles, long-context models see the full text.
+Dense models produce one vector per article.
+ColBERT models produce one vector per token (~2,000 vectors per average article).
+
+| Model                                                                                 | Year | Type              | Dims | Context |  Params | License    | Base / Fine-tuned by                     | Perf           |
+| :------------------------------------------------------------------------------------ | ---: | :---------------- | ---: | ------: | ------: | :--------- | :--------------------------------------- | :------------- |
+| [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)              | 2025 | Dense (decoder)   | 1024 |    32 K |   600 M | Apache 2.0 | Qwen3 (Alibaba)                          | 70.7 MTEB v2   |
+| [GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1)         | 2025 | ColBERT (encoder) |  128 |  8-32 K |   139 M | Apache 2.0 | ModernBERT (Answer.AI) / LightOn         | 88.4 LongEmbed |
+| [arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) | 2024 | Dense (encoder)   | 1024 |     8 K | 303 M ¹ | Apache 2.0 | XLM-R (Meta) → BGE-M3 (BAAI) / Snowflake | 55.6 BEIR      |
+| [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)        | 2024 | Dense (encoder)   |  768 |     8 K |   137 M | Apache 2.0 | NomicBERT (Nomic)                        | 62.3 MTEB v1   |
+| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)      | 2023 | Dense (decoder)   | 4096 |     4 K |   7.1 B | MIT        | Mistral-7B (Mistral AI) / Microsoft      | 66.6 MTEB v1   |
+
+¹ 568M total, 303M active (non-embedding parameters).
+
+### Compute Estimates
+
+All embeddings stored in FP16.
+Decoder models use [vLLM](https://github.com/vllm-project/vllm) with `--task embed`.
+Encoder models use [TEI](https://github.com/huggingface/text-embeddings-inference) with the Hopper Docker image.
+FP8 quantization can improve throughput ~1.5× with negligible quality loss.
+
+Token counts vary by tokenizer — CJK text produces ~1 token per 2-3 bytes, Latin/Cyrillic ~1 per 4-5 bytes.
+Average article length across all languages is ~400 tokens, but this is dragged down by millions of stubs in smaller wikis; English articles average ~2,700 tokens.
+
+| Model                  | Throughput | Total tokens |   Time | Vectors | Storage | Notes                          |
+| :--------------------- | ---------: | -----------: | -----: | ------: | ------: | :----------------------------- |
+| Qwen3-Embedding-0.6B   |  500 doc/s |         24 B |  1.4 d |  61.6 M |  126 GB | Full articles                  |
+| GTE-ModernColBERT-v1   |  800 doc/s |         24 B |  0.9 d |  24.3 B |  6.2 TB | ~400 token vectors per article |
+| arctic-embed-l-v2.0    |  800 doc/s |         28 B |  0.9 d |  61.6 M |  126 GB | Truncated at 8K tokens         |
+| nomic-embed-text-v1.5  | 1200 doc/s |         21 B |  0.6 d |  61.6 M |   95 GB | Truncated at 8K tokens         |
+| e5-mistral-7b-instruct |   50 doc/s |         21 B | 14.3 d |  61.6 M |  505 GB | Truncated at 4K tokens         |
+
+> Single H100 80 GB, full dataset — 61.6M articles, all 325 languages.
+
+## Metadata Enrichment
+
+### Compute Estimates
+
+## Dataset Layout
+
+Data is published on HuggingFace with one __config per model__, so users download only what they need.
+Embedding vectors are stored as `.fbin` (row-major binary: `uint32 rows, uint32 cols, float16/float32 data`) for direct compatibility with [USearch bench_cpp](https://github.com/unum-cloud/usearch/blob/main/BENCHMARKS.md) and the Big-ANN benchmark ecosystem.
+
+```
+unum-cloud/WikiVerse/
+├── README.md
+│
+├── corpus/
+│   ├── passages-en-00000-of-00050.parquet    # doc_id, title, text, section
+│   ├── passages-en-00001-of-00050.parquet
+│   └── ...
+│
+├── graph/
+│   ├── pagelinks-en.parquet                  # source_id, target_id
+│   ├── categories-en.parquet                 # page_id, category
+│   ├── wikidata-en.parquet                   # page_id, qid, entity_type
+│   └── ...
+│
+├── qwen3-embedding-0.6b/
+│   ├── base.6.6M.f16bin                      # 6.6M × 1024, float16
+│   ├── query.10K.f16bin                      # 10K × 1024, float16
+│   └── groundtruth.10K.ibin                  # 10K × 100 neighbors
+│
+├── e5-mistral-7b-instruct/
+│   ├── base.6.6M.f16bin                      # 6.6M × 4096, float16
+│   ├── query.10K.f16bin
+│   └── groundtruth.10K.ibin
+│
+├── arctic-embed-l-v2.0/
+│   ├── base.6.6M.f16bin                      # 6.6M × 1024, float16
+│   ├── query.10K.f16bin
+│   └── groundtruth.10K.ibin
+│
+├── nomic-embed-text-v1.5/
+│   ├── base.6.6M.f16bin                      # 6.6M × 768, float16
+│   ├── query.10K.f16bin
+│   └── groundtruth.10K.ibin
+│
+└── gte-moderncolbert-v1/
+    ├── base.6.6M.f16bin                      # ~13.2B × 128, float16
+    ├── query.10K.f16bin
+    └── groundtruth.10K.ibin
+```
+
+Load a specific model's embeddings:
+
+```python
+from datasets import load_dataset
+corpus = load_dataset("unum-cloud/WikiVerse", "corpus")
+```
+
+Or download binary vectors directly for C++/Rust benchmarking:
+
+```sh
+huggingface-cli download unum-cloud/WikiVerse \
+    qwen3-embedding-0.6b/base.6.6M.f16bin \
+    qwen3-embedding-0.6b/query.10K.f16bin \
+    qwen3-embedding-0.6b/groundtruth.10K.ibin
+```
+
+### Workflow
+
+The embedding pipeline is designed for multi-day runs on GPU servers with checkpoint/resume:
+
+```sh
+# 1. Download FineWiki articles
+python corpus.py --lang en --output corpus/
+
+# 2. Embed with each model (resume-safe — rerun after interruptions)
+python embed.py --model qwen3-0.6b --input corpus/ --output embeddings/ --resume
+python embed.py --model e5-mistral-7b --input corpus/ --output embeddings/ --resume
+python embed.py --model arctic-embed-l-v2 --input corpus/ --output embeddings/ --resume
+python embed.py --model nomic-v1.5 --input corpus/ --output embeddings/ --resume
+python embed.py --model gte-moderncolbert --input corpus/ --output embeddings/ --resume
+
+# 3. Extract graph metadata
+python graph.py --lang en --output graph/
+
+# 4. Compute ground truth for each model
+python ground_truth.py --embeddings embeddings/qwen3-0.6b/ --k 100 --queries 10000
+python ground_truth.py --embeddings embeddings/e5-mistral-7b/ --k 100 --queries 10000
+
+# 5. Upload to HuggingFace
+python upload.py --repo unum-cloud/WikiVerse
+```
+
+Each step is idempotent.
+Progress is tracked in `state/*.json` files — if a job dies (OOM, SSH drop, GPU error), rerunning the same command picks up from the last checkpoint.
+Adding a new embedding model requires only step 2 + step 4 — the corpus and graph are shared.
+
+## Hosting
+
+| Location                                                                           | Storage/mo (1 TB) | Egress/GB | Notes                                                            |
+| ---------------------------------------------------------------------------------- | ----------------- | --------- | ---------------------------------------------------------------- |
+| [HuggingFace Hub](https://huggingface.co/unum-cloud/WikiVerse)                     | Free              | Free      | Primary. Xet storage, unlimited public downloads                 |
+| [AWS S3](https://aws.amazon.com/s3/pricing/) Standard                              | $23.00            | $0.09     | S3-compatible mirror. Egress adds up fast for popular datasets   |
+| [Nebius Object Storage](https://docs.nebius.com/object-storage/resources/pricing/) | $15.05            | $0.015    | S3-compatible. ~35% cheaper storage, ~6× cheaper egress than AWS |
+
+## License
+
+The embedding pipeline code in this repository is licensed under [Apache 2.0](LICENSE).
+
+Dataset licensing depends on the components:
+
+- __Wikipedia text__: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+- __FineWiki extraction__: [Apache 2.0](https://huggingface.co/datasets/HuggingFaceFW/finewiki)
+- __Embeddings__: Governed by each model's license (see table above — all selected models use Apache 2.0 or MIT)
+- __Graph metadata__: Derived from Wikimedia/Wikidata dumps ([CC0](https://creativecommons.org/publicdomain/zero/1.0/) for Wikidata, [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) for Wikipedia)