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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'url', 'name', 'symbol'}) and 7 missing columns ({'embed_model', 'chunk_size_words', 'overlap_words', 'chunks', 'device', 'run_id', 'total_chunks'}).
This happened while the json dataset builder was generating data using
hf://datasets/eksakstri/nifty50-rag-data/nifty50_companies.json (at revision 57bdc207d45c1088426eae70e17dc36101844fd7), ['hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/chunk_metadata.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_companies.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_market_summary.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_snapshot.json'], ['hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/chunk_metadata.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_companies.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_market_summary.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_snapshot.json']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
symbol: string
name: string
url: string
to
{'run_id': Value('string'), 'embed_model': Value('string'), 'device': Value('string'), 'chunk_size_words': Value('int64'), 'overlap_words': Value('int64'), 'total_chunks': Value('int64'), 'chunks': {'char_count': Value('int64'), 'chunk_index': Value('int64'), 'chunk_total': Value('int64'), 'company': Value('string'), 'faiss_id': Value('int64'), 'ingested_at': Value('string'), 'pdf_file': Value('string'), 'pdf_path': Value('string'), 'run_id': Value('string')}}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'url', 'name', 'symbol'}) and 7 missing columns ({'embed_model', 'chunk_size_words', 'overlap_words', 'chunks', 'device', 'run_id', 'total_chunks'}).
This happened while the json dataset builder was generating data using
hf://datasets/eksakstri/nifty50-rag-data/nifty50_companies.json (at revision 57bdc207d45c1088426eae70e17dc36101844fd7), ['hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/chunk_metadata.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_companies.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_market_summary.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_snapshot.json'], ['hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/chunk_metadata.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_companies.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_market_summary.json', 'hf://datasets/eksakstri/nifty50-rag-data@57bdc207d45c1088426eae70e17dc36101844fd7/nifty50_snapshot.json']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
run_id string | embed_model string | device string | chunk_size_words int64 | overlap_words int64 | total_chunks int64 | chunks dict |
|---|---|---|---|---|---|---|
e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
"char_count": 3331,
"chunk_index": 0,
"chunk_total": 2,
"company": "ADANIENT",
"faiss_id": 0,
"ingested_at": "2026-07-06 17:39:41.943468+00:00",
"pdf_file": "ADANIENT_01042026184830_AELSEintimation01042026.pdf",
"pdf_path": "ADANIENT\\pdfs\\ADANIENT_01042026184830_AELSEintimation01042026.pdf",
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
"char_count": 1172,
"chunk_index": 1,
"chunk_total": 2,
"company": "ADANIENT",
"faiss_id": 1,
"ingested_at": "2026-07-06 17:39:41.943488+00:00",
"pdf_file": "ADANIENT_01042026184830_AELSEintimation01042026.pdf",
"pdf_path": "ADANIENT\\pdfs\\ADANIENT_01042026184830_AELSEintimation01042026.pdf",
"run_... |
e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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"company": "ADANIENT",
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"run_... |
e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
"char_count": 3331,
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"company": "ADANIENT",
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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"company": "ADANIENT",
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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"company": "ADANIENT",
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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"pdf_path": "ADANIENT\\pdfs\\ADANIENT_10062026195747_AEL_Intimationofacquis... |
e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
"char_count": 2064,
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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e357d42b-c9ae-46c3-ac13-9ff6f47ef4cd | all-MiniLM-L6-v2 | cpu | 500 | 50 | 61,128 | {
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