Datasets:

HaseebDev
/

pharmacy_rx_questions

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
niche_id: string
niche_name: string
category: string
record_count: int64
expected_fields: list<item: string>
  child 0, item: string
descriptive_fields: list<item: string>
  child 0, item: string
quality_score: int64
passed: bool
schema: struct<valid_records: int64, conformance_pct: double, missing_fields: struct<>, empty_fields: struct (... 27 chars omitted)
  child 0, valid_records: int64
  child 1, conformance_pct: double
  child 2, missing_fields: struct<>
  child 3, empty_fields: struct<>
  child 4, extra_fields: struct<>
uniqueness: struct<duplicate_rows: int64, duplicate_identity_values: int64, duplicate_fields: struct<>>
  child 0, duplicate_rows: int64
  child 1, duplicate_identity_values: int64
  child 2, duplicate_fields: struct<>
text_depth: struct<field_count: int64, avg_chars: double, min_chars: int64, avg_words: double, min_words: int64, (... 73 chars omitted)
  child 0, field_count: int64
  child 1, avg_chars: double
  child 2, min_chars: int64
  child 3, avg_words: double
  child 4, min_words: int64
  child 5, vocabulary_diversity: double
  child 6, total_tokens: int64
  child 7, unique_tokens: int64
similarity: struct<max_similarity: double, avg_similarity: double, near_duplicate_pairs: int64>
  child 0, max_similarity: double
  child 1, avg_similarity: double
  child 2, near_duplicate_pairs: int64
repetition: struct<repeated_ngram_count: int64, repeated_ngram_rate: double, top_repeated_phrases: list<item: nu (... 98 chars omitted)
  child 0, repeated_ngram_count: int64
  child 1, repeated_ngram_rate: double
  child 2, top_repeated_phrases: list<item: null>
      child 0, item: null
  child 3, frequency_threshold: int64
  child 4, raw_repeated_ngram_count: int64
  child 5, raw_repeated_ngram_rate: double
pii: struct<email_count: int64, non_synthetic_email_count: int64, phone_count: int64, non_synthetic_phone (... 14 chars omitted)
  child 0, email_count: int64
  child 1, non_synthetic_email_count: int64
  child 2, phone_count: int64
  child 3, non_synthetic_phone_count: int64
domain: struct<keyword_set_size: int64, matched_keywords: list<item: string>, keyword_coverage: double>
  child 0, keyword_set_size: int64
  child 1, matched_keywords: list<item: string>
      child 0, item: string
  child 2, keyword_coverage: double
balance: struct<fields: struct<>>
  child 0, fields: struct<>
findings: list<item: null>
  child 0, item: null
content: struct<rejected_record_count: int64, rejections: list<item: null>>
  child 0, rejected_record_count: int64
  child 1, rejections: list<item: null>
      child 0, item: null
verification: struct<all_rows_checked: int64, similarity_is_bounded: bool, max_similarity_pairs: int64>
  child 0, all_rows_checked: int64
  child 1, similarity_is_bounded: bool
  child 2, max_similarity_pairs: int64
data_sha256: string
repair_source_revision: string
pharmacist_advice: string
medication_name: string
inquiry_id: string
patient_question: string
patient_name: string
to
{'inquiry_id': Value('string'), 'patient_name': Value('string'), 'medication_name': Value('string'), 'patient_question': Value('string'), 'pharmacist_advice': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              niche_id: string
              niche_name: string
              category: string
              record_count: int64
              expected_fields: list<item: string>
                child 0, item: string
              descriptive_fields: list<item: string>
                child 0, item: string
              quality_score: int64
              passed: bool
              schema: struct<valid_records: int64, conformance_pct: double, missing_fields: struct<>, empty_fields: struct (... 27 chars omitted)
                child 0, valid_records: int64
                child 1, conformance_pct: double
                child 2, missing_fields: struct<>
                child 3, empty_fields: struct<>
                child 4, extra_fields: struct<>
              uniqueness: struct<duplicate_rows: int64, duplicate_identity_values: int64, duplicate_fields: struct<>>
                child 0, duplicate_rows: int64
                child 1, duplicate_identity_values: int64
                child 2, duplicate_fields: struct<>
              text_depth: struct<field_count: int64, avg_chars: double, min_chars: int64, avg_words: double, min_words: int64, (... 73 chars omitted)
                child 0, field_count: int64
                child 1, avg_chars: double
                child 2, min_chars: int64
                child 3, avg_words: double
                child 4, min_words: int64
                child 5, vocabulary_diversity: double
                child 6, total_tokens: int64
                child 7, unique_tokens: int64
              similarity: struct<max_similarity: double, avg_similarity: double, near_duplicate_pairs: int64>
                child 0, max_similarity: double
                child 1, avg_similarity: double
                child 2, near_duplicate_pairs: int64
              repetition: struct<repeated_ngram_count: int64, repeated_ngram_rate: double, top_repeated_phrases: list<item: nu (... 98 chars omitted)
                child 0, repeated_ngram_count: int64
                child 1, repeated_ngram_rate: double
                child 2, top_repeated_phrases: list<item: null>
                    child 0, item: null
                child 3, frequency_threshold: int64
                child 4, raw_repeated_ngram_count: int64
                child 5, raw_repeated_ngram_rate: double
              pii: struct<email_count: int64, non_synthetic_email_count: int64, phone_count: int64, non_synthetic_phone (... 14 chars omitted)
                child 0, email_count: int64
                child 1, non_synthetic_email_count: int64
                child 2, phone_count: int64
                child 3, non_synthetic_phone_count: int64
              domain: struct<keyword_set_size: int64, matched_keywords: list<item: string>, keyword_coverage: double>
                child 0, keyword_set_size: int64
                child 1, matched_keywords: list<item: string>
                    child 0, item: string
                child 2, keyword_coverage: double
              balance: struct<fields: struct<>>
                child 0, fields: struct<>
              findings: list<item: null>
                child 0, item: null
              content: struct<rejected_record_count: int64, rejections: list<item: null>>
                child 0, rejected_record_count: int64
                child 1, rejections: list<item: null>
                    child 0, item: null
              verification: struct<all_rows_checked: int64, similarity_is_bounded: bool, max_similarity_pairs: int64>
                child 0, all_rows_checked: int64
                child 1, similarity_is_bounded: bool
                child 2, max_similarity_pairs: int64
              data_sha256: string
              repair_source_revision: string
              pharmacist_advice: string
              medication_name: string
              inquiry_id: string
              patient_question: string
              patient_name: string
              to
              {'inquiry_id': Value('string'), 'patient_name': Value('string'), 'medication_name': Value('string'), 'patient_question': Value('string'), 'pharmacist_advice': Value('string')}
              because column names don't match

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Pharmacy_Rx_Questions (Synthetic B2B Dataset Preview)

Add me on Discord: xomohappy for access support, delivery questions, or product questions about this premade commercial dataset.

This is a premium, privacy-compliant, industry-safe synthetic dataset simulating Pharmacy Prescription Inquiries & Advisory Logs for B2B applications.

About this Dataset

This dataset is generated programmatically using large language models combined with a strict data curation and validation layer.

Privacy-safe: Contains synthetic names, phone numbers, and company identifiers.
Structured and verified: Schema-validated, formatted, and deduplicated to reduce model overfitting risk.
Target AI use case: Digital pharmacy apps building automated medication adherence bots.
Category: Healthcare

Schema Definition

Each record contains the following fields:

inquiry_id (str): Pre-assigned Inquiry ID (e.g. RX-FAQ-XXXX)
patient_name (str): Pre-assigned Patient Name
medication_name (str): Prescribed drug (e.g. Lisinopril, Metformin, Atorvastatin)
patient_question (str): Inquiry regarding side effects, drug interactions, or missed doses
pharmacist_advice (str): HIPAA-compliant, clinically accurate pharmacist advisory response

Get the Commercial Version

Need a larger dataset for production fine-tuning? The matching private repo is HaseebDev/pharmacy_rx_questions-commercial and is available under a commercial license.

Payment: Lemon Squeezy hosted checkout.
Delivery: Lemon Squeezy checkout with secure commercial delivery after purchase
Typical commercial package: 10,000+ records, schema documentation, and quality audit report.

Open the commercial access page

Quality Audit

Dataset Quality Report

Executive Summary

Metric	Value
Status	PASS
Quality Score	100/100
Records Audited	100
Niche	Pharmacy Prescription Inquiries & Advisory Logs
Category	Healthcare

Quality Gates

Dimension	Result
Schema Conformance	100.0% (100/100)
Duplicate Rows	0
Duplicate Identity Values	0
Non-Synthetic Phone Risk	0
Email Address Count	0
Max Fuzzy Similarity	0.601
Average Words Per Descriptive Field	96.72
Vocabulary Diversity	0.775
Repeated Phrase Count	0
Domain Keyword Coverage	0.3

Buyer Assurance

This dataset was checked with deterministic local tooling before publication. The audit verifies schema consistency, uniqueness, text depth, duplicate risk, repeated wording, domain terminology coverage, and obvious PII leakage patterns.

All rows receive schema and content checks. Cross-record similarity uses a bounded sample of pairs to keep generation fast; it does not establish that every possible pair is dissimilar. The score measures these automated checks, not downstream model performance or independent medical, legal, or regulatory validation.

Findings

No blocking quality findings detected.

Data SHA-256: 4929bd9490df1192e00a052ffd2d0b0572cbeed4ec6d3e86bb7d31c023da9ad7. Re-audited on 2026-10-08.

Repair: defective fictional narratives were corrected; record IDs and synthetic profile identities were retained. Legacy previews now cover all six scenario classes.

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Total file size:

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