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Forward-merge branch-24.02 to branch-24.04 #2130

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9 changes: 5 additions & 4 deletions docs/source/wiki_all_dataset.md
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The `wiki-all` dataset was created to stress vector search algorithms at scale with both a large number of vectors and dimensions. The entire dataset contains 88M vectors with 768 dimensions and is meant for testing the types of vectors one would typically encounter in retrieval augmented generation (RAG) workloads. The full dataset is ~251GB in size, which is intentionally larger than the typical memory of GPUs. The massive scale is intended to promote the use of compression and efficient out-of-core methods for both indexing and search.

The dataset is composed of all the available languages of in the [Cohere Wikipedia dataset](https://huggingface.co/datasets/Cohere/wikipedia-22-12). An [English version]( https://www.kaggle.com/datasets/jjinho/wikipedia-20230701) is also available.


The dataset is composed of English wiki texts from [Kaggle](https://www.kaggle.com/datasets/jjinho/wikipedia-20230701) and multi-lingual wiki texts from [Cohere Wikipedia](https://huggingface.co/datasets/Cohere/wikipedia-22-12).

Cohere's English Texts are older (2022) and smaller than the Kaggle English Wiki texts (2023) so the English texts have been removed from Cohere completely. The final Wiki texts include English Wiki from Kaggle and the other languages from Cohere. The English texts constitute 50% of the total text size.

To form the final dataset, the Wiki texts were chunked into 85 million 128-token pieces. For reference, Cohere chunks Wiki texts into 104-token pieces. Finally, the embeddings of each chunk were computed using the [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) embedding model. The resulting dataset is an embedding matrix of size 88 million by 768. Also included with the dataset is a query file containing 10k query vectors and a groundtruth file to evaluate nearest neighbors algorithms.
To form the final dataset, the Wiki texts were chunked into 85 million 128-token pieces. For reference, Cohere chunks Wiki texts into 104-token pieces. Finally, the embeddings of each chunk were computed using the [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) embedding model. The resulting dataset is an embedding matrix of size 88 million by 768. Also included with the dataset is a query file containing 10k query vectors and a groundtruth file to evaluate nearest neighbors algorithms.

## Getting the dataset

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## Using the dataset

After the dataset is downloaded and extracted to the `wiki_all_88M` directory (or `wiki_all_1M`/`wiki_all_10M` depending on whether the subsets are used), the files can be used in the benchmarking tool. The dataset name is `wiki_all` (or `wiki_all_1M`/`wiki_all_10M`), and the benchmarking tool can be used by specifying the appropriate name `--dataset wiki_all_88M` in the scripts.

## License info

The English wiki texts available on Kaggle come with the [CC BY-NCSA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license and the Cohere wikipedia data set comes with the [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license.