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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
lang_code: string
embedder: string
embedding_dim: int64
rows: int64
index_type: string
_output_all_columns: bool
_split: string
_format_type: null
_format_columns: null
_data_files: list<item: struct<filename: string>>
  child 0, item: struct<filename: string>
      child 0, filename: string
_format_kwargs: struct<>
_fingerprint: string
to
{'_data_files': List({'filename': Value('string')}), '_fingerprint': Value('string'), '_format_columns': Value('null'), '_format_kwargs': {}, '_format_type': Value('null'), '_output_all_columns': Value('bool'), '_split': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              lang_code: string
              embedder: string
              embedding_dim: int64
              rows: int64
              index_type: string
              _output_all_columns: bool
              _split: string
              _format_type: null
              _format_columns: null
              _data_files: list<item: struct<filename: string>>
                child 0, item: struct<filename: string>
                    child 0, filename: string
              _format_kwargs: struct<>
              _fingerprint: string
              to
              {'_data_files': List({'filename': Value('string')}), '_fingerprint': Value('string'), '_format_columns': Value('null'), '_format_kwargs': {}, '_format_type': Value('null'), '_output_all_columns': Value('bool'), '_split': Value('string')}
              because column names don't match

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Check out the documentation for more information.

Low-Resource RAG: Wikipedia FAISS Indexes (BGE-M3)

FAISS indexes over Wikipedia 2023 passages for four languages, embedded with BAAI/bge-m3. Source corpus: CohereLabs/wikipedia-2023-11-embed-multilingual-v3.

Each language folder has two files aligned row-by-row:

  • index.faiss — FAISS IndexFlatIP, dim=1024, L2-normalized BGE-M3 vectors
  • dataset/ — Arrow dataset with _id, title, text per passage

Languages

Language Code Passages Size
Hindi hi TBD 2.6G
Marathi mr TBD 975M
Nepali ne TBD 412M
Maithili mai TBD 66M
Bengali bn 767,965 3.7G
Gujarati gu 99,450 495M
Urdu ur 434,269 2.0G
Santali sat 43,500 209M
Tamil ta 685,975 3.3G
Telugu te 634,778 3.0G
Kannada kn 309,085 1.6G
Malayalam ml 384,795 1.9G
Punjabi pa 188,091 914M
Assamese as 76,093 376M

Usage

import faiss
from datasets import load_from_disk
from FlagEmbedding import BGEM3FlagModel

LANG = "hi"  # hi | mr | ne | mai
INDEX_DIR = "/path/to/indexes"

index = faiss.read_index(f"{INDEX_DIR}/{LANG}/index.faiss")
dataset = load_from_disk(f"{INDEX_DIR}/{LANG}/dataset")

model = BGEM3FlagModel("BAAI/bge-m3", use_fp16=True)
query_vec = model.encode(["your query"])["dense_vecs"]

scores, indices = index.search(query_vec, k=10)
results = dataset.select(indices[0])

Download

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Pika4028/low-resource-rag-indexes",
    repo_type="dataset",
    local_dir="/path/to/indexes",
    allow_patterns="hi/*",  # omit for all languages
)
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