Datasets:

gaultian
/

product-search-rag-data

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 127, in _split_generators
                  self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1977, in from_arrow_schema
                  else generate_from_arrow_type(field.type)
                       ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1634, in generate_from_arrow_type
                  return Value(dtype=_arrow_to_datasets_dtype(pa_type))
                                     ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 125, in _arrow_to_datasets_dtype
                  raise ValueError(f"Arrow type {arrow_type} does not have a datasets dtype equivalent.")
              ValueError: Arrow type map<string, string ('details')> does not have a datasets dtype equivalent.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Product Search RAG Data (Amazon Appliances)

Processed data and prebuilt search indexes for the Product Search RAG app.

Live demo: https://bm-semantic-hybrid-search.streamlit.app/

This repository hosts build artifacts for that app, not a general-purpose dataset. The app downloads these files at startup, since they are too large to keep in its git repository.

Source

Derived from the Appliances category of the Amazon Reviews 2023 dataset collected by McAuley Lab. Use of this data follows the terms of the original dataset. Please cite the original authors:

@article{hou2024bridging,
  title={Bridging Language and Items for Retrieval and Recommendation},
  author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian},
  journal={arXiv preprint arXiv:2403.03952},
  year={2024}
}

Files

File	Description
`processed.parquet`	20,000 products, merged from the first 20k metadata rows and first 200k review rows, joined on `parent_asin`.
`bm25_index.pkl`	BM25 index built with `rank_bm25` over title, features, description, categories, details and review text.
`semantic.index`	FAISS inner-product index of `all-MiniLM-L6-v2` embeddings over product metadata (no review text).
`semantic.ids.npy`	`parent_asin` for each row of `semantic.index`.
`rag_faiss/index.faiss`, `rag_faiss/index.pkl`	LangChain FAISS vector store of 500-character chunks (100 overlap), used by the RAG mode.

Columns in `processed.parquet`

parent_asin, product_title, features, description, categories, details, price, derived_avg_rating, n_reviews, review_text, candidate_review_title, candidate_review_text, candidate_review_helpful_vote

How it was built

See the project repository for the full pipeline (make process, make build-ir, make build-rag). Rebuilt files are uploaded with make upload-data.

Notes

bm25_index.pkl and rag_faiss/index.pkl are Python pickle files. Loading a pickle can run arbitrary code, so only load them if you trust this source.
Product text and reviews come from Amazon users and sellers and have not been filtered beyond HTML cleanup.

Downloads last month: 47

Total file size:

207 MB

Paper for gaultian/product-search-rag-data

Bridging Language and Items for Retrieval and Recommendation

Paper • 2403.03952 • Published Mar 6, 2024