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ee729e0 ab0e054 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 | """Smoke + dispatch tests for scripts/run_calibration.py.
Two failure modes this guards against:
1. Silent broken imports inside cmd_generate_outputs. The runner has no
module-level test coverage; a missing symbol like build_default_registry
will pass CI and fail at first invocation. test_module_imports asserts
the runner is importable.
2. Mixed-corpus calibration items routed to the wrong store. The spec
includes both k8s and fastapi questions. test_dispatch_routes_per_corpus
verifies each item goes to the orchestrator built for its corpus, and
test_unknown_corpus_raises verifies a clear error if the spec drifts
from the corpora the runner builds.
"""
from __future__ import annotations
import importlib
import json
import sys
from pathlib import Path
import pytest
REPO_ROOT = Path(__file__).resolve().parents[2]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
class _StubProvider:
def __init__(self, *args, **kwargs):
pass
class _StubEmbedder:
def __init__(self, *args, **kwargs):
pass
class _StubOrchestrator:
"""Records each run() call and returns a synthetic AgentResponse-shaped
object so cmd_generate_outputs can compute its hash + write its record.
"""
def __init__(self, corpus_name: str, calls: list) -> None:
self.corpus_name = corpus_name
self.calls = calls
async def run(self, *, question: str, system_prompt: str):
self.calls.append({"corpus": self.corpus_name, "question": question})
class _Source:
def __init__(self, s: str) -> None:
self.source = s
class _Resp:
answer = f"[{self.corpus_name}] answer to: {question}"
sources = [_Source(f"{self.corpus_name}/doc.md")]
ranked_sources = [f"{self.corpus_name}/doc.md"]
source_chunks = ["chunk text"]
return _Resp()
def test_module_imports():
"""Importing the runner must not raise. Catches broken symbol references
inside the module before they cost a calibration run."""
mod = importlib.import_module("scripts.run_calibration")
assert hasattr(mod, "cmd_generate_outputs")
assert hasattr(mod, "_build_corpus_orchestrator")
async def test_dispatch_routes_per_corpus(monkeypatch, tmp_path):
runner = importlib.import_module("scripts.run_calibration")
monkeypatch.setattr(
"agent_bench.core.provider.AnthropicProvider", _StubProvider
)
monkeypatch.setattr("agent_bench.rag.embedder.Embedder", _StubEmbedder)
calls: list = []
built_corpora: list = []
def fake_builder(cfg, corpus_name, embedder, provider):
built_corpora.append(corpus_name)
return _StubOrchestrator(corpus_name, calls)
monkeypatch.setattr(runner, "_build_corpus_orchestrator", fake_builder)
out_path = tmp_path / "system_outputs.json"
monkeypatch.setattr(runner, "SYSTEM_OUTPUTS", out_path)
await runner.cmd_generate_outputs(concurrency=2)
assert sorted(built_corpora) == ["fastapi", "k8s"]
spec = json.loads(runner.CALIBRATION_SPEC.read_text())
expected_corpus_by_id = {i["id"]: i["corpus"] for i in spec["items"]}
records = json.loads(out_path.read_text())
assert len(records) == len(expected_corpus_by_id)
seen_ids = set()
for rec in records:
item_id = rec["item_id"]
seen_ids.add(item_id)
expected = expected_corpus_by_id[item_id]
assert rec["corpus"] == expected
assert rec["answer"].startswith(f"[{expected}]")
assert rec["sources"] == [f"{expected}/doc.md"]
assert isinstance(rec["system_output_hash"], str)
assert len(rec["system_output_hash"]) == 64
assert seen_ids == set(expected_corpus_by_id.keys())
by_corpus: dict[str, int] = {}
for c in calls:
by_corpus[c["corpus"]] = by_corpus.get(c["corpus"], 0) + 1
expected_counts: dict[str, int] = {}
for cor in expected_corpus_by_id.values():
expected_counts[cor] = expected_counts.get(cor, 0) + 1
assert by_corpus == expected_counts
async def test_unknown_corpus_raises(monkeypatch, tmp_path):
runner = importlib.import_module("scripts.run_calibration")
monkeypatch.setattr(
"agent_bench.core.provider.AnthropicProvider", _StubProvider
)
monkeypatch.setattr("agent_bench.rag.embedder.Embedder", _StubEmbedder)
calls: list = []
def fake_builder(cfg, corpus_name, embedder, provider):
return _StubOrchestrator(corpus_name, calls)
monkeypatch.setattr(runner, "_build_corpus_orchestrator", fake_builder)
monkeypatch.setattr(
runner, "SYSTEM_OUTPUTS", tmp_path / "system_outputs.json"
)
spec = json.loads(runner.CALIBRATION_SPEC.read_text())
bogus_spec = {
"items": [
{**spec["items"][0], "corpus": "phantom_corpus"},
]
}
bogus_spec_path = tmp_path / "calibration_v1.json"
bogus_spec_path.write_text(json.dumps(bogus_spec))
monkeypatch.setattr(runner, "CALIBRATION_SPEC", bogus_spec_path)
with pytest.raises(KeyError) as excinfo:
await runner.cmd_generate_outputs(concurrency=1)
msg = str(excinfo.value)
assert "phantom_corpus" in msg
assert "not in cfg.corpora" in msg
assert spec["items"][0]["id"] in msg
def _write_jsonl(path: Path, rows: list[dict]) -> None:
path.write_text("\n".join(json.dumps(r) for r in rows) + "\n")
def test_compute_kappa_weights_returns_real_kappa(monkeypatch, tmp_path):
"""Two judges over 4 items with known agreement patterns. Cohen's κ
must come out positive for the high-agreement judge and zero for the
chance-agreement judge.
"""
runner = importlib.import_module("scripts.run_calibration")
labels_path = tmp_path / "labels.jsonl"
_write_jsonl(
labels_path,
[
{
"item_id": f"i{n}",
"dimension": "completeness",
"score": gold,
"abstained": False,
"system_output_hash": f"h{n}",
}
for n, gold in enumerate([2, 2, 1, 1])
],
)
monkeypatch.setattr(runner, "LABELS_PATH", labels_path)
sidecar = tmp_path / "predictions.jsonl"
rows = []
for n, gold in enumerate([2, 2, 1, 1]):
rows.append(
{
"judge_id": "good_completeness",
"system_output_hash": f"h{n}",
"score": gold,
}
)
rows.append(
{
"judge_id": "bad_completeness",
"system_output_hash": f"h{n}",
"score": 1,
}
)
_write_jsonl(sidecar, rows)
weights = runner._compute_kappa_weights(
sidecar,
"completeness",
expected_judge_ids={"good_completeness", "bad_completeness"},
)
assert weights["good_completeness"] == pytest.approx(1.0)
assert weights["bad_completeness"] == 0.0 # negative κ clipped to 0
def test_compute_kappa_weights_hard_errors_on_missing_member(monkeypatch, tmp_path):
"""Asymmetric coverage in the weights source must hard-error, not
silently return partial weights — that was the v1 bug that masked
gpt-4o-mini's exclusion."""
runner = importlib.import_module("scripts.run_calibration")
labels_path = tmp_path / "labels.jsonl"
_write_jsonl(
labels_path,
[
{
"item_id": "i0",
"dimension": "completeness",
"score": 2,
"abstained": False,
"system_output_hash": "h0",
}
],
)
monkeypatch.setattr(runner, "LABELS_PATH", labels_path)
sidecar = tmp_path / "predictions.jsonl"
_write_jsonl(
sidecar,
[
{
"judge_id": "haiku_completeness",
"system_output_hash": "h0",
"score": 2,
}
],
)
with pytest.raises(ValueError, match="symmetric coverage"):
runner._compute_kappa_weights(
sidecar,
"completeness",
expected_judge_ids={"haiku_completeness", "gpt4o_completeness"},
)
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