Datasets:

JuneYao
/

nihaisha-rag-assets

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
pdf_dir: string
store: string
embedding: string
embedding_backend: string
vector_kind: string
dims: int64
vector_dim: int64
window_size: int64
overlap: int64
unit_types: list<item: string>
  child 0, item: string
trace_dir: string
documents: list<item: struct<source_path: string, size_bytes: int64, paragraphs: int64, retrieval_units: int64, (... 321 chars omitted)
  child 0, item: struct<source_path: string, size_bytes: int64, paragraphs: int64, retrieval_units: int64, source_lay (... 309 chars omitted)
      child 0, source_path: string
      child 1, size_bytes: int64
      child 2, paragraphs: int64
      child 3, retrieval_units: int64
      child 4, source_layer: string
      child 5, relation_type: string
      child 6, authorship: string
      child 7, edition_verification: string
      child 8, relation_evidence_ids: list<item: string>
          child 0, item: string
      child 9, quality_flags: list<item: string>
          child 0, item: string
      child 10, strip_text_patterns: list<item: string>
          child 0, item: string
      child 11, strip_text_regexes: list<item: string>
          child 0, item: string
      child 12, exclude_pages: list<item: int64>
          child 0, item: int64
      child 13, strip_tilde_page_markers: bool
reference_link_evidence: list<item: struct<evidence_id: string, logical_source_path: string, page: int64, evidence_quote: str (... 5 chars omitted)
  child 0, item: struct<evidence_id: string, logical_source_path: string, page: i
...
symptom: int64
knowledge_extractor_version: string
knowledge_trace: string
knowledge_schema_version: int64
knowledge_migration_version: string
knowledge_structure_report: string
knowledge_structure_counts: struct<documents: int64, evidence_records: int64, entities: int64, entity_aliases: int64, relations: (... 7 chars omitted)
  child 0, documents: int64
  child 1, evidence_records: int64
  child 2, entities: int64
  child 3, entity_aliases: int64
  child 4, relations: int64
relation_evidence_coverage: double
rejected_candidates: int64
relation_types: struct<differentiates_from: int64, indicates_pattern: int64, supported_by: int64, uses_method: int64 (... 1 chars omitted)
  child 0, differentiates_from: int64
  child 1, indicates_pattern: int64
  child 2, supported_by: int64
  child 3, uses_method: int64
orphan_entities: int64
source_layers: struct<classic_primary: int64, course_primary: int64, reference_secondary: int64>
  child 0, classic_primary: int64
  child 1, course_primary: int64
  child 2, reference_secondary: int64
migration_version: string
schema_version: int64
counts: struct<documents: int64, evidence_records: int64, entities: int64, entity_aliases: int64, relations: (... 7 chars omitted)
  child 0, documents: int64
  child 1, evidence_records: int64
  child 2, entities: int64
  child 3, entity_aliases: int64
  child 4, relations: int64
review_statuses: struct<auto_accepted: int64, needs_review: int64>
  child 0, auto_accepted: int64
  child 1, needs_review: int64
to
{'schema_version': Value('int64'), 'migration_version': Value('string'), 'counts': {'documents': Value('int64'), 'evidence_records': Value('int64'), 'entities': Value('int64'), 'entity_aliases': Value('int64'), 'relations': Value('int64')}, 'rejected_candidates': Value('int64'), 'orphan_entities': Value('int64'), 'relation_evidence_coverage': Value('float64'), 'relation_types': {'differentiates_from': Value('int64'), 'indicates_pattern': Value('int64'), 'supported_by': Value('int64'), 'uses_method': Value('int64')}, 'review_statuses': {'auto_accepted': Value('int64'), 'needs_review': Value('int64')}, 'source_layers': {'classic_primary': Value('int64'), 'course_primary': Value('int64'), 'reference_secondary': Value('int64')}}
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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/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/python3.14/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 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
              pdf_dir: string
              store: string
              embedding: string
              embedding_backend: string
              vector_kind: string
              dims: int64
              vector_dim: int64
              window_size: int64
              overlap: int64
              unit_types: list<item: string>
                child 0, item: string
              trace_dir: string
              documents: list<item: struct<source_path: string, size_bytes: int64, paragraphs: int64, retrieval_units: int64, (... 321 chars omitted)
                child 0, item: struct<source_path: string, size_bytes: int64, paragraphs: int64, retrieval_units: int64, source_lay (... 309 chars omitted)
                    child 0, source_path: string
                    child 1, size_bytes: int64
                    child 2, paragraphs: int64
                    child 3, retrieval_units: int64
                    child 4, source_layer: string
                    child 5, relation_type: string
                    child 6, authorship: string
                    child 7, edition_verification: string
                    child 8, relation_evidence_ids: list<item: string>
                        child 0, item: string
                    child 9, quality_flags: list<item: string>
                        child 0, item: string
                    child 10, strip_text_patterns: list<item: string>
                        child 0, item: string
                    child 11, strip_text_regexes: list<item: string>
                        child 0, item: string
                    child 12, exclude_pages: list<item: int64>
                        child 0, item: int64
                    child 13, strip_tilde_page_markers: bool
              reference_link_evidence: list<item: struct<evidence_id: string, logical_source_path: string, page: int64, evidence_quote: str (... 5 chars omitted)
                child 0, item: struct<evidence_id: string, logical_source_path: string, page: i
              ...
              symptom: int64
              knowledge_extractor_version: string
              knowledge_trace: string
              knowledge_schema_version: int64
              knowledge_migration_version: string
              knowledge_structure_report: string
              knowledge_structure_counts: struct<documents: int64, evidence_records: int64, entities: int64, entity_aliases: int64, relations: (... 7 chars omitted)
                child 0, documents: int64
                child 1, evidence_records: int64
                child 2, entities: int64
                child 3, entity_aliases: int64
                child 4, relations: int64
              relation_evidence_coverage: double
              rejected_candidates: int64
              relation_types: struct<differentiates_from: int64, indicates_pattern: int64, supported_by: int64, uses_method: int64 (... 1 chars omitted)
                child 0, differentiates_from: int64
                child 1, indicates_pattern: int64
                child 2, supported_by: int64
                child 3, uses_method: int64
              orphan_entities: int64
              source_layers: struct<classic_primary: int64, course_primary: int64, reference_secondary: int64>
                child 0, classic_primary: int64
                child 1, course_primary: int64
                child 2, reference_secondary: int64
              migration_version: string
              schema_version: int64
              counts: struct<documents: int64, evidence_records: int64, entities: int64, entity_aliases: int64, relations: (... 7 chars omitted)
                child 0, documents: int64
                child 1, evidence_records: int64
                child 2, entities: int64
                child 3, entity_aliases: int64
                child 4, relations: int64
              review_statuses: struct<auto_accepted: int64, needs_review: int64>
                child 0, auto_accepted: int64
                child 1, needs_review: int64
              to
              {'schema_version': Value('int64'), 'migration_version': Value('string'), 'counts': {'documents': Value('int64'), 'evidence_records': Value('int64'), 'entities': Value('int64'), 'entity_aliases': Value('int64'), 'relations': Value('int64')}, 'rejected_candidates': Value('int64'), 'orphan_entities': Value('int64'), 'relation_evidence_coverage': Value('float64'), 'relation_types': {'differentiates_from': Value('int64'), 'indicates_pattern': Value('int64'), 'supported_by': Value('int64'), 'uses_method': Value('int64')}, 'review_statuses': {'auto_accepted': Value('int64'), 'needs_review': Value('int64')}, 'source_layers': {'classic_primary': Value('int64'), 'course_primary': Value('int64'), 'reference_secondary': Value('int64')}}
              because column names don't match

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Nihaisha RAG Runtime Assets

Public production runtime assets for the nihaisha-rag-prototype project.

Scope and provenance

This repository contains generated RAG runtime assets, not source PDF files.
The corpus has 23 documents: 10 course-primary documents, 1 classic-primary candidate, and 12 related-reference documents.
Related-reference documents are 关联参考资料（非倪海厦著作）. They must remain visibly separated from course-primary evidence.
The classic-primary candidate has not been independently edition-verified.
Extracted/OCR text is included inside the runtime database. Redistribution and downstream use remain subject to applicable copyright and local law.
Intended for educational source review and retrieval evaluation, not individualized medical diagnosis or treatment.

Download

Use the project downloader. It pins this production revision, resumes .part files, and verifies size and SHA256:

python3 -m nihaisha_kg download-assets

No Hugging Face account, token, CLI, or Git LFS is required.

Production asset set

File	Bytes	SHA256
`rag.sqlite`	2,195,161,088	`286b2389ed3096f6e378c1f869aecb00445d5335a98720679c647bfd3ca22997`
`vectors.faiss`	1,472,745,517	`ec5c576af53c56c6a22871ed0ebf8d3346435c200c54be6711a866fcb0a52cba`
`vector_ids.jsonl`	11,505,824	`dc90ebf490393e87ef998918242e4e8a41423507d260fa8f5afa6c6bc54ebf6f`
`manifest.json`	11,175	`fadfccd700e86c9522181f495ece1a5deab6dc838dac935273d709635e3597f3`
`knowledge_structure_report.json`	637	`b442c5053b81ab4bd48f85cb48f2ceec3d584c47993bbe8e04ac578d5917da9b`

Total production assets: 3,679,424,241 bytes (about 3.68 GB / 3.43 GiB).

Knowledge scale

13,012 paragraph/evidence records
359,557 BGE-M3 retrieval units, 1024 dimensions
24,720 knowledge units
42,329 guide nodes
15,193 relations

Runtime contract

All five production files belong together and should be downloaded from the same revision or production tag. Evidence shown to users must preserve source/page/paragraph traceback. Reference-layer retrieval is opt-in, while linked reference cards remain separately labelled navigation metadata.

Downloads last month: 749

Total file size:

3.68 GB