feat(benchmark): add build_exemplars_from_jsonl() for k-NN seed

scripts/benchmark_classifier.py

@@ -184,6 +184,37 @@ def discover_finetuned_models(models_dir: Path | None = None) -> list[dict]:
     return found
 
 
+def build_exemplars_from_jsonl(path: str, k_per_label: int = 10) -> dict[str, list[str]]:
+    """Sample up to k_per_label formatted email texts per label from a scored JSONL.
+
+    Formats each row as 'Subject: {subject}\n\n{body[:600]}' — the same format
+    EmbeddingKNNAdapter uses at classify() time.  Rows missing the 'label' key
+    are skipped silently.
+
+    Returns dict[label, list[str]] ready for EmbeddingKNNAdapter(exemplar_texts=...).
+    """
+    result: dict[str, list[str]] = {}
+    p = Path(path)
+    with p.open(encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                row = json.loads(line)
+            except json.JSONDecodeError:
+                continue
+            label = row.get("label")
+            if not label:
+                continue
+            texts = result.setdefault(label, [])
+            if len(texts) < k_per_label:
+                texts.append(
+                    f"Subject: {row.get('subject', '')}\n\n{row.get('body', '')[:600]}"
+                )
+    return result
+
+
 def _active_models(include_slow: bool = False) -> dict[str, dict[str, Any]]:
     """Return the active model registry, merged with any discovered fine-tuned models."""
     active: dict[str, dict[str, Any]] = {

tests/test_benchmark_classifier.py

@@ -166,3 +166,62 @@ def test_active_models_includes_discovered_finetuned(tmp_path):
 
     assert "avocet-deberta-small" in models
     assert isinstance(models["avocet-deberta-small"]["adapter_instance"], FineTunedAdapter)
+
+
+# ---- build_exemplars_from_jsonl() tests ----
+
+def test_build_exemplars_samples_up_to_k_per_label(tmp_path):
+    from scripts.benchmark_classifier import build_exemplars_from_jsonl
+    import json
+
+    rows = [{"subject": f"S{i}", "body": f"B{i}", "label": "rejected"} for i in range(15)]
+    rows.append({"subject": "Hire", "body": "Welcome", "label": "hired"})
+    f = tmp_path / "score.jsonl"
+    f.write_text("\n".join(json.dumps(r) for r in rows))
+
+    result = build_exemplars_from_jsonl(str(f), k_per_label=10)
+
+    assert len(result["rejected"]) == 10
+    assert len(result["hired"]) == 1
+    assert result["rejected"][0].startswith("Subject: S")
+
+
+def test_build_exemplars_formats_text_correctly(tmp_path):
+    from scripts.benchmark_classifier import build_exemplars_from_jsonl
+    import json
+
+    row = {"subject": "My Subject", "body": "My Body", "label": "neutral"}
+    f = tmp_path / "score.jsonl"
+    f.write_text(json.dumps(row))
+
+    result = build_exemplars_from_jsonl(str(f))
+
+    assert result["neutral"][0] == "Subject: My Subject\n\nMy Body"
+
+
+def test_build_exemplars_skips_rows_missing_label(tmp_path):
+    from scripts.benchmark_classifier import build_exemplars_from_jsonl
+    import json
+
+    rows = [
+        {"subject": "A", "body": "B", "label": "neutral"},
+        {"subject": "No label here", "body": "Body"},
+    ]
+    f = tmp_path / "score.jsonl"
+    f.write_text("\n".join(json.dumps(r) for r in rows))
+
+    result = build_exemplars_from_jsonl(str(f))
+    assert list(result.keys()) == ["neutral"]
+
+
+def test_build_exemplars_truncates_body_at_600(tmp_path):
+    from scripts.benchmark_classifier import build_exemplars_from_jsonl
+    import json
+
+    row = {"subject": "S", "body": "x" * 800, "label": "neutral"}
+    f = tmp_path / "score.jsonl"
+    f.write_text(json.dumps(row))
+
+    result = build_exemplars_from_jsonl(str(f))
+    body_part = result["neutral"][0].split("\n\n", 1)[1]
+    assert len(body_part) == 600