feat: Allow custom model and params with huggingface_base_url

mem0/embeddings/huggingface.py

@@ -18,6 +18,7 @@ def __init__(self, config: Optional[BaseEmbedderConfig] = None):
 
         if config.huggingface_base_url:
             self.client = OpenAI(base_url=config.huggingface_base_url)
+            self.config.model = self.config.model or "tei"
         else:
             self.config.model = self.config.model or "multi-qa-MiniLM-L6-cos-v1"
 
@@ -36,6 +37,8 @@ def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]
             list: The embedding vector.
         """
         if self.config.huggingface_base_url:
-            return self.client.embeddings.create(input=text, model="tei").data[0].embedding
+            return self.client.embeddings.create(
+                input=text, model=self.config.model, **self.config.model_kwargs
+            ).data[0].embedding
         else:
             return self.model.encode(text, convert_to_numpy=True).tolist()

tests/embeddings/test_huggingface_embeddings.py

@@ -70,3 +70,34 @@ def test_embed_with_custom_embedding_dims(mock_sentence_transformer):
     assert embedder.config.embedding_dims == 768
 
     assert result == [1.0, 1.1, 1.2]
+
+
+def test_embed_with_huggingface_base_url():
+    config = BaseEmbedderConfig(
+        huggingface_base_url="http://localhost:8080",
+        model="my-custom-model",
+        model_kwargs={"truncate": True},
+    )
+    with patch("mem0.embeddings.huggingface.OpenAI") as mock_openai:
+        mock_client = Mock()
+        mock_openai.return_value = mock_client
+
+        # Create a mock for the response object and its attributes
+        mock_embedding_response = Mock()
+        mock_embedding_response.embedding = [0.1, 0.2, 0.3]
+
+        mock_create_response = Mock()
+        mock_create_response.data = [mock_embedding_response]
+
+        mock_client.embeddings.create.return_value = mock_create_response
+
+        embedder = HuggingFaceEmbedding(config)
+        result = embedder.embed("Hello from custom endpoint")
+
+        mock_openai.assert_called_once_with(base_url="http://localhost:8080")
+        mock_client.embeddings.create.assert_called_once_with(
+            input="Hello from custom endpoint",
+            model="my-custom-model",
+            truncate=True,
+        )
+        assert result == [0.1, 0.2, 0.3]