Aria AI Operations Research Portfolio
Collection
Enterprise OR, optimization, and decomposition demos by Aria AI • 151 items • Updated
positive_pairs int64 | negative_pairs int64 | doc_hit_rate float64 | keyword_hit_rate float64 | abstain_rate float64 | thresholds dict | passed bool | index dict |
|---|---|---|---|---|---|---|---|
12 | 3 | 1 | 1 | 0.6667 | {
"min_doc_hit_rate": 0.8,
"min_keyword_hit_rate": 0.7,
"min_abstain_rate": 0.66
} | true | {
"backend": "hybrid_tfidf_bm25",
"n_chunks": 48,
"word_vocab": 1569,
"char_vocab": 6361
} |
OR Knowledge Copilot Corpus
Multi-layer operations-research knowledge base used by OR Knowledge Copilot.
Each instance is stored as six chunks:
- Natural language
- Mathematical formulation
- Pyomo template
- MiniZinc template
- Solver output
- Explanation of binding constraints
Files
chunks.jsonl— retrieval unitsqa_pairs.jsonl— labeled questions including out-of-scope abstention casesbenchmark_report.json/eval_results.json— published retrieval metricstaxonomy.json— domain and layer countsmanifest.json— dataset identity
Metrics
Document hit rate, keyword coverage, and abstention rate are computed by scripts/build_assets.py and stored in eval_results.json.
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