feat(classifier): add EmbeddingKNNAdapter skeleton and constructor tests

scripts/classifier_adapters.py

@@ -20,6 +20,7 @@ __all__ = [
     "GLiClassAdapter",
     "RerankerAdapter",
     "FineTunedAdapter",
+    "EmbeddingKNNAdapter",
 ]
 
 LABELS: list[str] = [
@@ -381,3 +382,75 @@ class FineTunedAdapter(ClassifierAdapter):
         text = f"{subject} [SEP] {body[:400]}"
         result = self._pipeline(text)
         return result[0]["label"]
+
+
+class EmbeddingKNNAdapter(ClassifierAdapter):
+    """k-NN email classifier using Ollama /v1/embeddings via cf-orch allocation.
+
+    load():
+      1. Allocates an Ollama instance from cf-orch (POST /api/services/ollama/allocate).
+         Falls back to ollama_url directly if orch allocation fails or is not configured.
+      2. Pre-embeds all exemplar texts and stores per-label vector lists.
+
+    classify(subject, body):
+      Embeds the input email, computes cosine similarity against all stored exemplar
+      vectors, and majority-votes the top-k labels (default k=3). Tie-break: label
+      with the highest mean similarity score among tied vote counts wins.
+
+    unload():
+      Releases the cf-orch allocation (DELETE .../allocations/{id}) and clears state.
+    """
+
+    def __init__(
+        self,
+        name: str,
+        model_id: str,
+        *,
+        k: int = 3,
+        orch_url: str = "",
+        ollama_url: str = "",
+        exemplar_texts: dict[str, list[str]] | None = None,
+    ) -> None:
+        self._name = name
+        self._model_id = model_id
+        self._k = k
+        self._orch_url = orch_url
+        self._ollama_url = ollama_url
+        self._exemplar_texts: dict[str, list[str]] = (
+            exemplar_texts if exemplar_texts is not None else DEFAULT_EXEMPLARS
+        )
+        self._exemplar_embeddings: dict[str, list[list[float]]] = {}
+        self._node_url: str = ""
+        self._allocation_id: str = ""
+        self._orch_url_used: str = ""
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    @property
+    def model_id(self) -> str:
+        return self._model_id
+
+    def _resolve_urls(self) -> tuple[str, str]:
+        if self._orch_url or self._ollama_url:
+            return self._orch_url, self._ollama_url
+        import yaml  # noqa: PLC0415
+        cfg_path = Path(__file__).parent.parent / "config" / "label_tool.yaml"
+        cfg: dict = {}
+        if cfg_path.exists():
+            try:
+                cfg = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) or {}
+            except yaml.YAMLError:
+                pass
+        cforch = cfg.get("cforch", {}) or {}
+        return cforch.get("coordinator_url", ""), cforch.get("ollama_url", "")
+
+    def load(self) -> None:
+        raise NotImplementedError
+
+    def unload(self) -> None:
+        raise NotImplementedError
+
+    def classify(self, subject: str, body: str) -> str:
+        raise NotImplementedError

tests/test_classifier_adapters.py

@@ -322,3 +322,43 @@ def test_default_exemplars_strings_are_formatted_correctly():
             assert "\n\n" in text, (
                 f"{label!r} exemplar missing double-newline separator: {text[:50]!r}"
             )
+
+# ---- EmbeddingKNNAdapter constructor tests ----
+
+def test_embedding_knn_is_classifier_adapter():
+    from scripts.classifier_adapters import EmbeddingKNNAdapter, ClassifierAdapter
+    adapter = EmbeddingKNNAdapter(
+        "test-knn", "nomic-embed-text",
+        k=3, orch_url="http://orch:7700", ollama_url="http://ollama:11434",
+    )
+    assert isinstance(adapter, ClassifierAdapter)
+
+
+def test_embedding_knn_name_and_model_id():
+    from scripts.classifier_adapters import EmbeddingKNNAdapter
+    adapter = EmbeddingKNNAdapter(
+        "embed-knn-nomic", "nomic-embed-text",
+        k=3, orch_url="http://orch:7700", ollama_url="http://ollama:11434",
+    )
+    assert adapter.name == "embed-knn-nomic"
+    assert adapter.model_id == "nomic-embed-text"
+
+
+def test_embedding_knn_uses_default_exemplars_when_none_given():
+    from scripts.classifier_adapters import EmbeddingKNNAdapter, DEFAULT_EXEMPLARS
+    adapter = EmbeddingKNNAdapter(
+        "test", "nomic-embed-text",
+        k=3, orch_url="http://orch:7700", ollama_url="http://ollama:11434",
+    )
+    assert adapter._exemplar_texts is DEFAULT_EXEMPLARS
+
+
+def test_embedding_knn_accepts_custom_exemplars():
+    from scripts.classifier_adapters import EmbeddingKNNAdapter
+    custom = {"rejected": ["Sorry, we went with others."]}
+    adapter = EmbeddingKNNAdapter(
+        "test", "nomic-embed-text",
+        k=3, orch_url="http://orch:7700", ollama_url="http://ollama:11434",
+        exemplar_texts=custom,
+    )
+    assert adapter._exemplar_texts is custom