[BUG] fix bug when receive embedding from vertex api

1. change the way to check key in response.

chromadb/utils/embedding_functions.py

@@ -109,7 +109,8 @@ def __init__(self, model_name: str = "shibing624/text2vec-base-chinese"):
 
     def __call__(self, input: Documents) -> Embeddings:
         return cast(
-            Embeddings, self._model.encode(list(input), convert_to_numpy=True).tolist()
+            Embeddings, self._model.encode(
+                list(input), convert_to_numpy=True).tolist()
         )  # noqa E501
 
 
@@ -222,7 +223,8 @@ def __call__(self, input: Documents) -> Embeddings:
 
             # Return just the embeddings
             return cast(
-                Embeddings, [result["embedding"] for result in sorted_embeddings]
+                Embeddings, [result["embedding"]
+                             for result in sorted_embeddings]
             )
 
 
@@ -431,7 +433,8 @@ def __init__(self, preferred_providers: Optional[List[str]] = None) -> None:
         reraise=True,
         stop=stop_after_attempt(3),
         wait=wait_random(min=1, max=3),
-        retry=retry_if_exception(lambda e: "does not match expected SHA256" in str(e)),
+        retry=retry_if_exception(
+            lambda e: "does not match expected SHA256" in str(e)),
     )
     def _download(self, url: str, fname: str, chunk_size: int = 1024) -> None:
         resp = requests.get(url, stream=True)
@@ -466,7 +469,7 @@ def _forward(self, documents: List[str], batch_size: int = 32) -> npt.NDArray:
         self.model = cast(self.ort.InferenceSession, self.model)
         all_embeddings = []
         for i in range(0, len(documents), batch_size):
-            batch = documents[i : i + batch_size]
+            batch = documents[i: i + batch_size]
             encoded = [self.tokenizer.encode(d) for d in batch]
             input_ids = np.array([e.ids for e in encoded])
             attention_mask = np.array([e.attention_mask for e in encoded])
@@ -525,7 +528,8 @@ def model(self) -> "InferenceSession":
         so.log_severity_level = 3
 
         return self.ort.InferenceSession(
-            os.path.join(self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME, "model.onnx"),
+            os.path.join(self.DOWNLOAD_PATH,
+                         self.EXTRACTED_FOLDER_NAME, "model.onnx"),
             # Since 1.9 onnyx runtime requires providers to be specified when there are multiple available - https://onnxruntime.ai/docs/api/python/api_summary.html
             # This is probably not ideal but will improve DX as no exceptions will be raised in multi-provider envs
             providers=self._preferred_providers,
@@ -546,7 +550,8 @@ def _download_model_if_not_exists(self) -> None:
             "tokenizer.json",
             "vocab.txt",
         ]
-        extracted_folder = os.path.join(self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME)
+        extracted_folder = os.path.join(
+            self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME)
         onnx_files_exist = True
         for f in onnx_files:
             if not os.path.exists(os.path.join(extracted_folder, f)):
@@ -563,7 +568,8 @@ def _download_model_if_not_exists(self) -> None:
             ):
                 self._download(
                     url=self.MODEL_DOWNLOAD_URL,
-                    fname=os.path.join(self.DOWNLOAD_PATH, self.ARCHIVE_FILENAME),
+                    fname=os.path.join(self.DOWNLOAD_PATH,
+                                       self.ARCHIVE_FILENAME),
                 )
             with tarfile.open(
                 name=os.path.join(self.DOWNLOAD_PATH, self.ARCHIVE_FILENAME),
@@ -676,8 +682,9 @@ def __call__(self, input: Documents) -> Embeddings:
                 self._api_url, json={"instances": [{"content": text}]}
             ).json()
 
-            if "predictions" in response:
-                for prediction in response["predictions"]:
+            predictions = response.get("predictions")
+            if predictions:
+                for prediction in predictions:
                     embeddings.append(prediction["embeddings"]["values"])
 
         return embeddings
@@ -745,7 +752,7 @@ 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"
+        self, api_key: str = "", api_url="https://infer.roboflow.com"
     ) -> None:
         """
         Create a RoboflowEmbeddingFunction.
@@ -758,7 +765,7 @@ 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")
@@ -776,7 +783,8 @@ def __call__(self, input: Union[Documents, Images]) -> Embeddings:
 
                 buffer = BytesIO()
                 image.save(buffer, format="JPEG")
-                base64_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
+                base64_image = base64.b64encode(
+                    buffer.getvalue()).decode("utf-8")
 
                 infer_clip_payload = {
                     "image": {
@@ -793,7 +801,7 @@ def __call__(self, input: Union[Documents, Images]) -> Embeddings:
                 result = res.json()['embeddings']
 
                 embeddings.append(result[0])
-            
+
             elif is_document(item):
                 infer_clip_payload = {
                     "text": input,
@@ -810,7 +818,7 @@ def __call__(self, input: Union[Documents, Images]) -> Embeddings:
 
         return embeddings
 
-      
+
 class AmazonBedrockEmbeddingFunction(EmbeddingFunction[Documents]):
     def __init__(
         self,
@@ -897,7 +905,8 @@ def __call__(self, input: Documents) -> Embeddings:
         """
         # Call HuggingFace Embedding Server API for each document
         return cast(
-            Embeddings, self._session.post(self._api_url, json={"inputs": input}).json()
+            Embeddings, self._session.post(
+                self._api_url, json={"inputs": input}).json()
         )
 
 
@@ -910,7 +919,8 @@ def create_langchain_embedding(langchain_embdding_fn: Any):  # type: ignore
         )
 
     class ChromaLangchainEmbeddingFunction(
-        LangchainEmbeddings, EmbeddingFunction[Union[Documents, Images]]  # type: ignore
+        # type: ignore
+        LangchainEmbeddings, EmbeddingFunction[Union[Documents, Images]]
     ):
         """
         This class is used as bridge between langchain embedding functions and custom chroma embedding functions.
@@ -926,14 +936,16 @@ def __init__(self, embedding_function: LangchainEmbeddings) -> None:
             self.embedding_function = embedding_function
 
         def embed_documents(self, documents: Documents) -> List[List[float]]:
-            return self.embedding_function.embed_documents(documents)  # type: ignore
+            # type: ignore
+            return self.embedding_function.embed_documents(documents)
 
         def embed_query(self, query: str) -> List[float]:
             return self.embedding_function.embed_query(query)  # type: ignore
 
         def embed_image(self, uris: List[str]) -> List[List[float]]:
             if hasattr(self.embedding_function, "embed_image"):
-                return self.embedding_function.embed_image(uris)  # type: ignore
+                # type: ignore
+                return self.embedding_function.embed_image(uris)
             else:
                 raise ValueError(
                     "The provided embedding function does not support image embeddings."
@@ -963,7 +975,7 @@ 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).
@@ -1019,7 +1031,7 @@ def __call__(self, input: Documents) -> Embeddings:
             ],
         )
 
-      
+
 # List of all classes in this module
 _classes = [
     name