Fix model download for ONNX embedder (#976)

## Description of changes
The current function is looking for the tar.gz file instead of checking
if the folder already exists, so if the tar.gz gets deleted after
extraction, it downloads it again.. This PR resolves this and checks for
the model in the extracted folder before attempting to download or
extract again.

## Test plan
By using it

## Documentation Changes
I didn't find any documentation about how this does the download.

chromadb/utils/embedding_functions.py

@@ -271,7 +271,7 @@ def __init__(self) -> None:
 
     # Borrowed from https://gist.github.com/yanqd0/c13ed29e29432e3cf3e7c38467f42f51
     # Download with tqdm to preserve the sentence-transformers experience
-    def _download(self, url: str, fname: Path, chunk_size: int = 1024) -> None:
+    def _download(self, url: str, fname: str, chunk_size: int = 1024) -> None:
         resp = requests.get(url, stream=True)
         total = int(resp.headers.get("content-length", 0))
         with open(fname, "wb") as file, self.tqdm(
@@ -326,14 +326,18 @@ def _forward(self, documents: List[str], batch_size: int = 32) -> npt.NDArray:
     def _init_model_and_tokenizer(self) -> None:
         if self.model is None and self.tokenizer is None:
             self.tokenizer = self.Tokenizer.from_file(
-                str(self.DOWNLOAD_PATH / self.EXTRACTED_FOLDER_NAME / "tokenizer.json")
+                os.path.join(
+                    self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME, "tokenizer.json"
+                )
             )
             # max_seq_length = 256, for some reason sentence-transformers uses 256 even though the HF config has a max length of 128
             # https://github.com/UKPLab/sentence-transformers/blob/3e1929fddef16df94f8bc6e3b10598a98f46e62d/docs/_static/html/models_en_sentence_embeddings.html#LL480
             self.tokenizer.enable_truncation(max_length=256)
             self.tokenizer.enable_padding(pad_id=0, pad_token="[PAD]", length=256)
             self.model = self.ort.InferenceSession(
-                str(self.DOWNLOAD_PATH / self.EXTRACTED_FOLDER_NAME / "model.onnx")
+                os.path.join(
+                    self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME, "model.onnx"
+                )
             )
 
     def __call__(self, texts: Documents) -> Embeddings:
@@ -344,16 +348,35 @@ def __call__(self, texts: Documents) -> Embeddings:
         return res
 
     def _download_model_if_not_exists(self) -> None:
+        onnx_files = [
+            "config.json",
+            "model.onnx",
+            "special_tokens_map.json",
+            "tokenizer_config.json",
+            "tokenizer.json",
+            "vocab.txt",
+        ]
+        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)):
+                onnx_files_exist = False
+                break
         # Model is not downloaded yet
-        if not os.path.exists(self.DOWNLOAD_PATH / self.ARCHIVE_FILENAME):
+        if not onnx_files_exist:
             os.makedirs(self.DOWNLOAD_PATH, exist_ok=True)
-            self._download(
-                self.MODEL_DOWNLOAD_URL, self.DOWNLOAD_PATH / self.ARCHIVE_FILENAME
-            )
+            if not os.path.exists(
+                os.path.join(self.DOWNLOAD_PATH, self.ARCHIVE_FILENAME)
+            ):
+                self._download(
+                    url=self.MODEL_DOWNLOAD_URL,
+                    fname=os.path.join(self.DOWNLOAD_PATH, self.ARCHIVE_FILENAME),
+                )
             with tarfile.open(
-                self.DOWNLOAD_PATH / self.ARCHIVE_FILENAME, "r:gz"
+                name=os.path.join(self.DOWNLOAD_PATH, self.ARCHIVE_FILENAME),
+                mode="r:gz",
             ) as tar:
-                tar.extractall(self.DOWNLOAD_PATH)
+                tar.extractall(path=self.DOWNLOAD_PATH)
 
 
 def DefaultEmbeddingFunction() -> Optional[EmbeddingFunction]:
@@ -410,7 +433,6 @@ def __init__(
         self._session.headers.update({"Authorization": f"Bearer {api_key}"})
 
     def __call__(self, texts: Documents) -> Embeddings:
-
         embeddings = []
         for text in texts:
             response = self._session.post(