fix: load HuggingFace models once

datastew/embedding.py

@@ -171,14 +171,22 @@ def get_embeddings(self, messages: List[str]) -> Sequence[Sequence[float]]:
 
 
 class HuggingFaceAdapter(EmbeddingModel):
+    _model_cache = {}
+    _load_count = 0 # For testing
+
     def __init__(self, model_name: str = "sentence-transformers/all-MiniLM-L6-v2", cache: bool = False):
         """Initialize the Hugging Face adapter with a specified model name.
 
         :param model_name: The model name for sentence transformers, defaults to sentence-transformers/all-MiniLM-L6-v2.
         :param cache: Enable or disable caching, defaults to False.
         """
         super().__init__(model_name, cache)
-        self.model = SentenceTransformer(model_name)
+
+        if model_name not in self._model_cache:
+            HuggingFaceAdapter._load_count += 1
+            self._model_cache[model_name] = SentenceTransformer(model_name)
+
+        self.model = self._model_cache[model_name]
 
     def get_embedding(self, text: str) -> Sequence[float]:
         """Retrieve an embedding for a single text input using MPnet.

tests/test_embedding.py

@@ -63,3 +63,20 @@ def test_caching_get_embeddings(self):
 
         for emb1, emb2 in zip(embeddings1, embeddings2):
             self.assertSequenceEqual(emb1, emb2)
+
+    def test_huggingface_model_loaded_once(self):
+        # Reset class-level cache and counter before test
+        HuggingFaceAdapter._model_cache.clear()
+        HuggingFaceAdapter._load_count = 0
+
+        # Instantiate multiple adapters
+        adapter1 = HuggingFaceAdapter(cache=True)
+        adapter2 = HuggingFaceAdapter(cache=True)
+        adapter3 = HuggingFaceAdapter(cache=True)
+
+        # All adapters should use the same underlying model object
+        self.assertIs(adapter1.model, adapter2.model)
+        self.assertIs(adapter2.model, adapter3.model)
+
+        # And the model should have been loaded only once
+        self.assertEqual(HuggingFaceAdapter._load_count, 1)