hf encoder tests

tests/unit/encoders/test_huggingface.py

+import pytest
+import numpy as np
+from semantic_router.encoders.huggingface import HuggingFaceEncoder
+
+
+class TestHuggingFaceEncoder:
+    def test_huggingface_encoder(self):
+        encoder = HuggingFaceEncoder()
+        test_docs = ["This is a test", "This is another test"]
+        embeddings = encoder(test_docs)
+        assert isinstance(embeddings, list)
+        assert len(embeddings) == len(test_docs)
+        assert all(isinstance(embedding, list) for embedding in embeddings)
+        assert all(len(embedding) > 0 for embedding in embeddings)
+
+    def test_huggingface_encoder_normalized_embeddings(self):
+        encoder = HuggingFaceEncoder()
+        docs = ["This is a test document.", "Another test document."]
+        unnormalized_embeddings = encoder(docs, normalize_embeddings=False)
+        normalized_embeddings = encoder(docs, normalize_embeddings=True)
+        assert len(unnormalized_embeddings) == len(normalized_embeddings)
+
+        for unnormalized, normalized in zip(
+            unnormalized_embeddings, normalized_embeddings
+        ):
+            norm_unnormalized = np.linalg.norm(unnormalized, ord=2)
+            norm_normalized = np.linalg.norm(normalized, ord=2)
+            # Ensure the norm of the normalized embeddings is approximately 1
+            assert np.isclose(norm_normalized, 1.0)
+            # Ensure the normalized embeddings are actually normalized versions of unnormalized embeddings
+            np.testing.assert_allclose(
+                normalized,
+                np.divide(unnormalized, norm_unnormalized),
+                rtol=1e-5,
+                atol=1e-5,  # Adjust tolerance levels
+            )
+
+    def test_huggingface_encoder_invalid_pooling_strategy(self):
+        encoder = HuggingFaceEncoder()
+        docs = ["This is a test document.", "Another test document."]
+        with pytest.raises(ValueError):
+            encoder(docs, pooling_strategy="invalid_strategy")