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Fix model download for ONNX embedder #976

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merged 6 commits into from
Aug 15, 2023
Merged

Fix model download for ONNX embedder #976

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eee9fc5
Update embedding_functions.py
Josh-XT Aug 13, 2023
5131e22
Make consistent
Josh-XT Aug 13, 2023
0cff795
Updates
Josh-XT Aug 13, 2023
68190ef
Download archive only if it doesn't exist
Josh-XT Aug 13, 2023
66b7814
Fix paths
Josh-XT Aug 13, 2023
ca54ad4
Check if each file exists for ONNX
Josh-XT Aug 14, 2023
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42 changes: 32 additions & 10 deletions chromadb/utils/embedding_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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(
Expand Down Expand Up @@ -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:
Expand All @@ -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]:
Expand Down Expand Up @@ -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(
Expand Down