Datasets:

aevionai
/

aevion-codebase-rag-benchmark

license: apache-2.0
language:
  - en
tags:
  - rag
  - code-retrieval
  - verified-ai
  - constitutional-halt
  - bft-consensus
  - aevion
size_categories:
  - 10K<n<100K
task_categories:
  - question-answering
  - document-retrieval

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.