Datasets:

aevionai
/

aevion-codebase-rag-benchmark

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
auto_tune: bool
codebase_root: string
corpus_source: string
generated_at: string
include_oracle: bool
max_constraints: int64
methods: struct<instructed: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>, naive: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>, verified_consensus: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>, verified_structural: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>, verified_structural_ensemble: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>>
min_constraints: int64
n_docs: int64
n_queries_eval: int64
n_queries_generated: int64
n_queries_requested: int64
n_queries_tune: int64
retrieval_config: struct<ensemble_top_k: int64, ensemble_vote_threshold: double, final_match_ratio: double, min_match_ratio: double, min_step_k: int64, noise_weight: double, step_k_ratio: double>
seed: int64
tune_target: string
vs
complexity: string
doc_id: string
function_type: string
has_docstring: string
keyword: string
layer: string
module: string
source_line: int64
source_path: string
text: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 588, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              auto_tune: bool
              codebase_root: string
              corpus_source: string
              generated_at: string
              include_oracle: bool
              max_constraints: int64
              methods: struct<instructed: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>, naive: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>, verified_consensus: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>, verified_structural: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>, verified_structural_ensemble: struct<avg_f1: double, avg_precision: double, avg_recall: double, exact_match_rate: double, queries: double>>
              min_constraints: int64
              n_docs: int64
              n_queries_eval: int64
              n_queries_generated: int64
              n_queries_requested: int64
              n_queries_tune: int64
              retrieval_config: struct<ensemble_top_k: int64, ensemble_vote_threshold: double, final_match_ratio: double, min_match_ratio: double, min_step_k: int64, noise_weight: double, step_k_ratio: double>
              seed: int64
              tune_target: string
              vs
              complexity: string
              doc_id: string
              function_type: string
              has_docstring: string
              keyword: string
              layer: string
              module: string
              source_line: int64
              source_path: string
              text: string

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YAML Metadata Warning: The task_categories "document-retrieval" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Aevion Codebase RAG Benchmark

Verified structured-retrieval benchmark extracted from a real Python codebase (968 source files, 21,149 chunks) with cryptographically signed partition proofs.

What's in this dataset

File	Description
`codebase_corpus.jsonl`	21,149 Python code chunks with 6-field structural metadata
`codebase_queries.jsonl`	300 enterprise query-decomposition pairs
`partition_proofs.jsonl`	XGML-signed proof bundles per query
`benchmark_results.csv`	Precision/recall/F1 per retrieval method (60 eval queries)
`benchmark_summary.json`	Aggregate metrics and auto-tuning parameters
`tuning_summary.json`	Grid-search results across 10K synthetic docs

Corpus Schema

Each chunk in codebase_corpus.jsonl has:

{
  "doc_id": "chunk_000000",
  "text": "module.ClassName (path/to/file.py:20)",
  "layer": "core",
  "module": "verification",
  "function_type": "class",
  "keyword": "hash",
  "complexity": "simple",
  "has_docstring": "yes",
  "source_path": "core/python/...",
  "source_line": 20
}

Benchmark Results (60 eval queries)

Method	Precision	Recall	F1	Exact Match
naive	0.516	0.657	0.425	11.7%
instructed	1.000	0.385	0.463	23.3%
verified_structural	1.000	0.385	0.463	23.3%
verified_consensus	1.000	0.437	0.503	31.7%
verified_structural_ensemble	1.000	0.459	0.527	33.3%

Key finding: Structural + ensemble retrieval achieves 100% precision (zero irrelevant chunks) vs. 51.6% for naive keyword search.

Method

AST extraction: Python files parsed with ast module → class/function/method chunks
6-field structural metadata: layer, module, function_type, keyword, complexity, has_docstring
Constitutional Halt labeling: VarianceHaltMonitor (σ > 2.5x threshold) as automatic quality gate
XGML proof bundles: Ed25519-signed proof chain on every partition plan

Aevion Verifiable AI — source codebase
Patent US 63/896,282 — Variance Halt + Constitutional AI halts

License

Apache 2.0 — freely use for research and commercial applications.

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