Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
set1_exact_match: struct<Dense: struct<chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: do (... 4624 chars omitted)
child 0, Dense: struct<chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: dou (... 195 chars omitted)
child 0, chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: double, MRR: double, ND (... 29 chars omitted)
child 0, P@1: double
child 1, R@1: double
child 2, P@3: double
child 3, R@3: double
child 4, P@5: double
child 5, R@5: double
child 6, MRR: double
child 7, NDCG@3: double
child 8, NDCG@5: double
child 1, source_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: double, MRR: double, ND (... 29 chars omitted)
child 0, P@1: double
child 1, R@1: double
child 2, P@3: double
child 3, R@3: double
child 4, P@5: double
child 5, R@5: double
child 6, MRR: double
child 7, NDCG@3: double
child 8, NDCG@5: double
child 1, BM25: struct<chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: dou (... 195 chars omitted)
child 0, chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: double, MRR: double, ND (... 29 chars omitted)
child 0, P@1: double
chi
...
hild 3, R@3: double
child 4, P@5: double
child 5, R@5: double
child 6, MRR: double
child 7, NDCG@3: double
child 8, NDCG@5: double
child 8, source_metrics: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: double, MRR: double, ND (... 29 chars omitted)
child 0, P@1: double
child 1, R@1: double
child 2, P@3: double
child 3, R@3: double
child 4, P@5: double
child 5, R@5: double
child 6, MRR: double
child 7, NDCG@3: double
child 8, NDCG@5: double
faiss_vectors: int64
n_chunks: int64
manual_annotation_instruction: struct<file_to_edit: string, column: string, grade_3: string, grade_2: string, grade_1: string, grad (... 12 chars omitted)
child 0, file_to_edit: string
child 1, column: string
child 2, grade_3: string
child 3, grade_2: string
child 4, grade_1: string
child 5, grade_0: string
n_answerable: int64
n_queries_total: int64
invalid_gold_chunk_ids_total: int64
outputs: struct<chunk_id_audit_summary: string, gold_chunk_preview: string, retrieval_candidate_review: strin (... 2 chars omitted)
child 0, chunk_id_audit_summary: string
child 1, gold_chunk_preview: string
child 2, retrieval_candidate_review: string
embedding_model: string
query_file: string
n_unanswerable: int64
to
{'query_file': Value('string'), 'n_queries_total': Value('int64'), 'n_answerable': Value('int64'), 'n_unanswerable': Value('int64'), 'n_chunks': Value('int64'), 'faiss_vectors': Value('int64'), 'embedding_model': Value('string'), 'invalid_gold_chunk_ids_total': Value('int64'), 'outputs': {'chunk_id_audit_summary': Value('string'), 'gold_chunk_preview': Value('string'), 'retrieval_candidate_review': Value('string')}, 'manual_annotation_instruction': {'file_to_edit': Value('string'), 'column': Value('string'), 'grade_3': Value('string'), 'grade_2': Value('string'), 'grade_1': Value('string'), 'grade_0': 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 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
set1_exact_match: struct<Dense: struct<chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: do (... 4624 chars omitted)
child 0, Dense: struct<chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: dou (... 195 chars omitted)
child 0, chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: double, MRR: double, ND (... 29 chars omitted)
child 0, P@1: double
child 1, R@1: double
child 2, P@3: double
child 3, R@3: double
child 4, P@5: double
child 5, R@5: double
child 6, MRR: double
child 7, NDCG@3: double
child 8, NDCG@5: double
child 1, source_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: double, MRR: double, ND (... 29 chars omitted)
child 0, P@1: double
child 1, R@1: double
child 2, P@3: double
child 3, R@3: double
child 4, P@5: double
child 5, R@5: double
child 6, MRR: double
child 7, NDCG@3: double
child 8, NDCG@5: double
child 1, BM25: struct<chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: dou (... 195 chars omitted)
child 0, chunk_level: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: double, MRR: double, ND (... 29 chars omitted)
child 0, P@1: double
chi
...
hild 3, R@3: double
child 4, P@5: double
child 5, R@5: double
child 6, MRR: double
child 7, NDCG@3: double
child 8, NDCG@5: double
child 8, source_metrics: struct<P@1: double, R@1: double, P@3: double, R@3: double, P@5: double, R@5: double, MRR: double, ND (... 29 chars omitted)
child 0, P@1: double
child 1, R@1: double
child 2, P@3: double
child 3, R@3: double
child 4, P@5: double
child 5, R@5: double
child 6, MRR: double
child 7, NDCG@3: double
child 8, NDCG@5: double
faiss_vectors: int64
n_chunks: int64
manual_annotation_instruction: struct<file_to_edit: string, column: string, grade_3: string, grade_2: string, grade_1: string, grad (... 12 chars omitted)
child 0, file_to_edit: string
child 1, column: string
child 2, grade_3: string
child 3, grade_2: string
child 4, grade_1: string
child 5, grade_0: string
n_answerable: int64
n_queries_total: int64
invalid_gold_chunk_ids_total: int64
outputs: struct<chunk_id_audit_summary: string, gold_chunk_preview: string, retrieval_candidate_review: strin (... 2 chars omitted)
child 0, chunk_id_audit_summary: string
child 1, gold_chunk_preview: string
child 2, retrieval_candidate_review: string
embedding_model: string
query_file: string
n_unanswerable: int64
to
{'query_file': Value('string'), 'n_queries_total': Value('int64'), 'n_answerable': Value('int64'), 'n_unanswerable': Value('int64'), 'n_chunks': Value('int64'), 'faiss_vectors': Value('int64'), 'embedding_model': Value('string'), 'invalid_gold_chunk_ids_total': Value('int64'), 'outputs': {'chunk_id_audit_summary': Value('string'), 'gold_chunk_preview': Value('string'), 'retrieval_candidate_review': Value('string')}, 'manual_annotation_instruction': {'file_to_edit': Value('string'), 'column': Value('string'), 'grade_3': Value('string'), 'grade_2': Value('string'), 'grade_1': Value('string'), 'grade_0': Value('string')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
pretty_name: AI Muon Collider RAG language:
- en tags:
- particle-physics
- muon-collider
- rag
- retrieval-augmented-generation
- high-energy-physics ---# QA result to paper outputs
Copy both scripts into:
/eos/home-r/rjiang/RAG_muon_collider/
Then run:
chmod +x run_qa20_and_make_outputs.sh
./run_qa20_and_make_outputs.sh data/eval/queries_eval_v4_template.json 20
Or manually:
python evaluate_agentic_v3.py \
--query_file data/eval/queries_eval_v4_template.json \
--dense_weight 0.9 \
--bm25_weight 0.1 \
--judge_answers \
--qa_systems hybrid,agentic_v3,oracle \
--max_queries 20 \
--output data/eval/eval_results_agentic_v3_qa20.json
python make_qa_paper_outputs.py \
--input data/eval/eval_results_agentic_v3_qa20.json \
--outdir paper_outputs_qa20
Outputs:
- paper_outputs_qa20/figures/*.png
- paper_outputs_qa20/tables_qa_results.tex
- paper_outputs_qa20/figures_include.tex
- paper_outputs_qa20/section_results_discussion.tex
Put the figures into Overleaf figures/, then paste the LaTeX tables and text into your paper.
AI Muon Collider RAG
A retrieval-augmented generation framework and evaluation dataset for literature related to muon collider physics.
Repository structure
main_pdf/
PDF documents used as the primary source corpus for the RAG system.
main_markdown/
Markdown/text representations extracted from the source PDFs. These files are used as machine-readable inputs for document chunking and retrieval.
data/index/
Pre-built retrieval resources used by the RAG system, including the vector/dense index, document chunks, and BM25-related files.
data/eval/
Evaluation datasets and outputs used to benchmark the retrieval and question-answering performance of the system.
RAG pipeline
The overall workflow is approximately:
PDF literature → text/Markdown extraction → document chunking → dense + sparse indexing → retrieval → LLM-based question answering → evaluation
Usage
The repository contains scripts for building the retrieval index, running the RAG chatbot, and evaluating its performance.
See the individual Python scripts for the current implementation.
Disclaimer
This repository is intended for research and development purposes. Source publications retain their respective copyrights and licenses.
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