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8c1b9fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | """Render reports/*.json into Markdown tables for paper/README inclusion.
Reads whatever reports already exist under reports/ (produced by `make eval`,
`make bench`, `make bench-rag`, `make bench-modelfit`,
`make bench-visual-grounding`) and writes reports/paper_tables.md. Never
invents a table for a report that doesn't exist — missing reports are noted,
not backfilled with placeholder numbers.
"""
from __future__ import annotations
import json
from auralynq.config import get_settings
def _provenance_line(report: dict) -> str:
p = report.get("provenance") or {}
commit = p.get("git_commit", "unknown")[:12]
ts = p.get("generated_at", "unknown")
dataset = p.get("dataset_version", "unknown")
return f"*commit `{commit}` · generated {ts} · dataset: {dataset}*"
def _eval_table(report: dict) -> str:
lines = [
"### Retrieval comparison\n",
"| Metric | " + " | ".join(report["retrieval"].keys()) + " |",
]
lines.append("|---|" + "---|" * len(report["retrieval"]))
metrics = ["recall_at_k", "ndcg_at_10", "mrr", "precision_at_k", "latency_p50_ms"]
for m in metrics:
row = [str(report["retrieval"][variant].get(m, "—")) for variant in report["retrieval"]]
lines.append(f"| {m} | " + " | ".join(row) + " |")
lines.append("")
lines.append(_provenance_line(report))
return "\n".join(lines)
def _hotpotqa_table(report: dict) -> str:
variants = report["retrieval"]
lines = [
"### Retrieval comparison — multi-hop HotpotQA\n",
"| Metric | " + " | ".join(variants.keys()) + " |",
"|---|" + "---|" * len(variants),
]
for m in ["recall_at_k", "ndcg_at_10", "mrr", "precision_at_k", "latency_p50_ms"]:
row = [str(variants[v].get(m, "—")) for v in variants]
lines.append(f"| {m} | " + " | ".join(row) + " |")
ragas = (report.get("agentic") or {}).get("ragas") or {}
if ragas:
lines.append("")
lines.append("### Answer quality — full agentic pipeline (RAGAS proxy)\n")
lines.append("| Faithfulness | Answer relevancy | Context precision |")
lines.append("|---:|---:|---:|")
lines.append(
f"| {ragas.get('faithfulness', '—')} | {ragas.get('answer_relevancy', '—')} "
f"| {ragas.get('context_precision', '—')} |"
)
lines.append("")
lines.append(_provenance_line(report))
return "\n".join(lines)
def _bench_table(report: dict) -> str:
lines = [
"### Qdrant quantization trade-off\n",
"| Quantization | Recall@k | Memory (bytes) | Latency (ms) |",
"|---|---:|---:|---:|",
]
for name, q in report["quantization"].items():
lines.append(f"| {name} | {q['recall_at_k']} | {q['memory_bytes']} | {q['latency_ms']} |")
lines.append("")
lines.append(_provenance_line(report))
return "\n".join(lines)
def _modelfit_table(report: dict) -> str:
lines = [
f"### ModelFit rankings (task={report.get('task') or 'any'})\n",
"| Model | Score | Label | Quant | Estimate? |",
"|---|---:|---|---|---|",
]
for r in report["rankings"]:
lines.append(
f"| {r['model_id']} | {r['overall_score']} | {r['label']} | "
f"{r['best_quantization']} | {'yes' if r['estimate_used'] else 'no'} |"
)
lines.append("")
lines.append(_provenance_line(report))
return "\n".join(lines)
def _visual_grounding_table(report: dict) -> str:
lines = ["### Visual grounding stage rates\n", "| Stage | Count | Rate |", "|---|---:|---:|"]
for stage, count in report["stage_counts"].items():
lines.append(f"| {stage} | {count} | {report['stage_rate'].get(stage, 0)} |")
lines.append("")
lines.append(_provenance_line(report))
return "\n".join(lines)
def _rag_bench_table(report: dict) -> str:
m = report["metrics"]
lines = [
"### RAG-quality benchmark\n",
"| Model | Groundedness | Citation coverage | Abstention accuracy |",
"|---|---:|---:|---:|",
f"| {report['model_id']} | {m.get('groundedness', '—')} | "
f"{m.get('citation_coverage', '—')} | {m.get('abstention_accuracy', '—')} |",
]
if m.get("warnings"):
lines.append("")
lines.append(f"Warnings: {'; '.join(m['warnings'])}")
return "\n".join(lines)
_TABLE_BUILDERS = {
"eval_report.json": ("Retrieval & Answer Quality (`make eval`)", _eval_table),
"eval_hotpotqa_report.json": (
"Multi-Hop QA (`python scripts/bench_hotpotqa.py`)",
_hotpotqa_table,
),
"bench_report.json": ("Vector Index Quantization (`make bench`)", _bench_table),
"modelfit_bench_report.json": ("ModelFit Index (`make bench-modelfit`)", _modelfit_table),
"visual_grounding_report.json": (
"Visual Grounding (`make bench-visual-grounding`)",
_visual_grounding_table,
),
"rag_bench_report.json": ("RAG Quality (`make bench-rag`)", _rag_bench_table),
}
def run(write_report: bool = True) -> str:
s = get_settings()
s.ensure_dirs()
sections = [
"# Auralynq Benchmark Tables\n",
"Generated from `reports/*.json` — see each section's provenance line "
"for the exact commit, timestamp, and dataset that produced it. "
"Nothing below is hand-written.\n",
]
missing: list[str] = []
for filename, (title, builder) in _TABLE_BUILDERS.items():
path = s.reports_dir / filename
if not path.exists():
missing.append(filename)
continue
report = json.loads(path.read_text(encoding="utf-8"))
sections.append(f"## {title}\n\n{builder(report)}\n")
if missing:
sections.append(
"## Not yet generated\n\n"
+ "\n".join(f"- `{m}` — run the matching `make` target to produce it." for m in missing)
)
doc = "\n".join(sections)
if write_report:
out = s.reports_dir / "paper_tables.md"
out.write_text(doc, encoding="utf-8")
return doc
if __name__ == "__main__":
print(run())
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