feat: add universal sentence encoder embedding function

chromadb/utils/embedding_functions.py

@@ -743,9 +743,7 @@ def __call__(self, input: Union[Documents, Images]) -> Embeddings:
 
 
 class RoboflowEmbeddingFunction(EmbeddingFunction[Union[Documents, Images]]):
-    def __init__(
-        self, api_key: str = "", api_url = "https://infer.roboflow.com"
-    ) -> None:
+    def __init__(self, api_key: str = "", api_url="https://infer.roboflow.com") -> None:
         """
         Create a RoboflowEmbeddingFunction.
 
@@ -757,7 +755,7 @@ def __init__(
             api_key = os.environ.get("ROBOFLOW_API_KEY")
 
         self._api_url = api_url
-        self._api_key = api_key 
+        self._api_key = api_key
 
         try:
             self._PILImage = importlib.import_module("PIL.Image")
@@ -789,10 +787,10 @@ def __call__(self, input: Union[Documents, Images]) -> Embeddings:
                     json=infer_clip_payload,
                 )
 
-                result = res.json()['embeddings']
+                result = res.json()["embeddings"]
 
                 embeddings.append(result[0])
-            
+
             elif is_document(item):
                 infer_clip_payload = {
                     "text": input,
@@ -803,13 +801,13 @@ def __call__(self, input: Union[Documents, Images]) -> Embeddings:
                     json=infer_clip_payload,
                 )
 
-                result = res.json()['embeddings']
+                result = res.json()["embeddings"]
 
                 embeddings.append(result[0])
 
         return embeddings
 
-      
+
 class AmazonBedrockEmbeddingFunction(EmbeddingFunction[Documents]):
     def __init__(
         self,
@@ -900,6 +898,22 @@ def __call__(self, input: Documents) -> Embeddings:
         )
 
 
+class UniversalSentenceEncoderEmbeddingFunction(EmbeddingFunction[Documents]):
+    def __init__(
+        self, model_name: str = "https://tfhub.dev/google/universal-sentence-encoder/4"
+    ):
+        try:
+            import tensorflow_hub as hub
+        except ImportError:
+            raise ValueError(
+                "The tensorflow_hub python package is not installed. Please install it with `pip install tensorflow_hub`"
+            )
+        self._model = hub.load(model_name)
+
+    def __call__(self, input: Documents) -> Embeddings:
+        return cast(Embeddings, self._model(input).numpy().tolist())
+
+
 def create_langchain_embedding(langchain_embdding_fn: Any):  # type: ignore
     try:
         from langchain_core.embeddings import Embeddings as LangchainEmbeddings
@@ -962,7 +976,7 @@ def __call__(self, input: Documents) -> Embeddings:  # type: ignore
 
     return ChromaLangchainEmbeddingFunction(embedding_function=langchain_embdding_fn)
 
- 
+
 class OllamaEmbeddingFunction(EmbeddingFunction[Documents]):
     """
     This class is used to generate embeddings for a list of texts using the Ollama Embedding API (https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings).
@@ -1018,7 +1032,7 @@ def __call__(self, input: Documents) -> Embeddings:
             ],
         )
 
-      
+
 # List of all classes in this module
 _classes = [
     name