| --- |
| 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: |
|
|
| ```json |
| { |
| "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 |
|
|
| 1. **AST extraction**: Python files parsed with `ast` module → class/function/method chunks |
| 2. **6-field structural metadata**: layer, module, function_type, keyword, complexity, has_docstring |
| 3. **Constitutional Halt labeling**: VarianceHaltMonitor (σ > 2.5x threshold) as automatic quality gate |
| 4. **XGML proof bundles**: Ed25519-signed proof chain on every partition plan |
|
|
| ## Related |
|
|
| - [Aevion Verifiable AI](https://github.com/aevionai/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. |
|
|
