Improve models cache progressbar (#406)

* chore: Remove typing hints of Python less than 3.9

* chore: Removed optional from cache as it cannot be undefined

* improve: Turned off progress bar of huggingface models if cached

README.md

@@ -28,10 +28,10 @@ pip install fastembed-gpu
 
 ```python
 from fastembed import TextEmbedding
-from typing import List
+
 
 # Example list of documents
-documents: List[str] = [
+documents: list[str] = [
     "This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.",
     "fastembed is supported by and maintained by Qdrant.",
 ]
@@ -139,11 +139,10 @@ embeddings = list(model.embed(images))
 
 ### 🔄 Rerankers
 ```python
-from typing import List
 from fastembed.rerank.cross_encoder import TextCrossEncoder
 
 query = "Who is maintaining Qdrant?"
-documents: List[str] = [
+documents: list[str] = [
     "This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.",
     "fastembed is supported by and maintained by Qdrant.",
 ]

docs/examples/Hindi_Tamil_RAG_with_Navarasa7B.ipynb

@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:45:24.814968Z",
@@ -58,8 +58,6 @@
    },
    "outputs": [],
    "source": [
-    "from typing import List\n",
-    "\n",
     "import numpy as np\n",
     "from datasets import load_dataset\n",
     "from peft import AutoPeftModelForCausalLM\n",
@@ -72,11 +70,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "hf_token = <YOUR_HF_TOKEN_HERE>  # Get your token from https://huggingface.co/settings/token, needed for Gemma weights"
+    "hf_token = \"<YOUR_HF_TOKEN_HERE>\"  # Get your token from https://huggingface.co/settings/token, needed for Gemma weights"
    ]
   },
   {
@@ -246,7 +244,7 @@
    },
    "outputs": [],
    "source": [
-    "context_embeddings: List[np.ndarray] = list(\n",
+    "context_embeddings: list[np.ndarray] = list(\n",
     "    embedding_model.embed(contexts)\n",
     ")  # Note the list() call - this is a generator"
    ]

docs/examples/SPLADE_with_FastEmbed.ipynb

@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:49:20.516644Z",
@@ -56,8 +56,7 @@
    },
    "outputs": [],
    "source": [
-    "from fastembed import SparseTextEmbedding, SparseEmbedding\n",
-    "from typing import List"
+    "from fastembed import SparseTextEmbedding, SparseEmbedding"
    ]
   },
   {
@@ -134,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:49:28.624109Z",
@@ -143,7 +142,7 @@
    },
    "outputs": [],
    "source": [
-    "documents: List[str] = [\n",
+    "documents: list[str] = [\n",
     "    \"Chandrayaan-3 is India's third lunar mission\",\n",
     "    \"It aimed to land a rover on the Moon's surface - joining the US, China and Russia\",\n",
     "    \"The mission is a follow-up to Chandrayaan-2, which had partial success\",\n",
@@ -157,7 +156,7 @@
     "    \"Chandrayaan-3 was launched from the Satish Dhawan Space Centre in Sriharikota\",\n",
     "    \"Chandrayaan-3 was launched earlier in the year 2023\",\n",
     "]\n",
-    "sparse_embeddings_list: List[SparseEmbedding] = list(\n",
+    "sparse_embeddings_list: list[SparseEmbedding] = list(\n",
     "    model.embed(documents, batch_size=6)\n",
     ")  # batch_size is optional, notice the generator"
    ]
@@ -235,7 +234,9 @@
    "source": [
     "# Let's print the first 5 features and their weights for better understanding.\n",
     "for i in range(5):\n",
-    "    print(f\"Token at index {sparse_embeddings_list[0].indices[i]} has weight {sparse_embeddings_list[0].values[i]}\")"
+    "    print(\n",
+    "        f\"Token at index {sparse_embeddings_list[0].indices[i]} has weight {sparse_embeddings_list[0].values[i]}\"\n",
+    "    )"
    ]
   },
   {
@@ -261,7 +262,9 @@
     "import json\n",
     "from transformers import AutoTokenizer\n",
     "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(SparseTextEmbedding.list_supported_models()[0][\"sources\"][\"hf\"])"
+    "tokenizer = AutoTokenizer.from_pretrained(\n",
+    "    SparseTextEmbedding.list_supported_models()[0][\"sources\"][\"hf\"]\n",
+    ")"
    ]
   },
   {
@@ -326,7 +329,9 @@
     "        token_weight_dict[token] = weight\n",
     "\n",
     "    # Sort the dictionary by weights\n",
-    "    token_weight_dict = dict(sorted(token_weight_dict.items(), key=lambda item: item[1], reverse=True))\n",
+    "    token_weight_dict = dict(\n",
+    "        sorted(token_weight_dict.items(), key=lambda item: item[1], reverse=True)\n",
+    "    )\n",
     "    return token_weight_dict\n",
     "\n",
     "\n",

docs/index.md

@@ -26,14 +26,14 @@ pip install fastembed
 ```python
 from fastembed import TextEmbedding
 
-documents: List[str] = [
+documents: list[str] = [
     "passage: Hello, World!",
     "query: Hello, World!",
     "passage: This is an example passage.",
     "fastembed is supported by and maintained by Qdrant."
 ]
 embedding_model = TextEmbedding()
-embeddings: List[np.ndarray] = embedding_model.embed(documents)
+embeddings: list[np.ndarray] = embedding_model.embed(documents)
 ```
 
 ## Usage with Qdrant

fastembed/common/model_management.py

@@ -7,7 +7,11 @@ from typing import Any, Optional
 
 import requests
 from huggingface_hub import snapshot_download
-from huggingface_hub.utils import RepositoryNotFoundError
+from huggingface_hub.utils import (
+    RepositoryNotFoundError,
+    disable_progress_bars,
+    enable_progress_bars,
+)
 from loguru import logger
 from tqdm import tqdm
 
@@ -93,7 +97,7 @@ class ModelManagement:
     def download_files_from_huggingface(
         cls,
         hf_source_repo: str,
-        cache_dir: Optional[str] = None,
+        cache_dir: str,
         extra_patterns: Optional[list[str]] = None,
         local_files_only: bool = False,
         **kwargs,
@@ -119,6 +123,12 @@ class ModelManagement:
         if extra_patterns is not None:
             allow_patterns.extend(extra_patterns)
 
+        snapshot_dir = Path(cache_dir) / f"models--{hf_source_repo.replace('/', '--')}"
+        is_cached = snapshot_dir.exists()
+
+        if is_cached:
+            disable_progress_bars()
+
         return snapshot_download(
             repo_id=hf_source_repo,
             allow_patterns=allow_patterns,
@@ -265,6 +275,8 @@ class ModelManagement:
                             f"Could not download model from HuggingFace: {e} "
                             "Falling back to other sources."
                         )
+                finally:
+                    enable_progress_bars()
             if url_source or local_files_only:
                 try:
                     return cls.retrieve_model_gcs(

fastembed/common/utils.py

@@ -1,13 +1,13 @@
 import os
-import tempfile
-from itertools import islice
-from pathlib import Path
-from typing import Generator, Iterable, Optional, Union
-import unicodedata
 import sys
-import numpy as np
 import re
-from typing import Set
+import tempfile
+import unicodedata
+from pathlib import Path
+from itertools import islice
+from typing import Generator, Iterable, Optional, Union
+
+import numpy as np
 
 
 def normalize(input_array, p=2, dim=1, eps=1e-12) -> np.ndarray:
@@ -45,7 +45,7 @@ def define_cache_dir(cache_dir: Optional[str] = None) -> Path:
     return cache_path
 
 
-def get_all_punctuation() -> Set[str]:
+def get_all_punctuation() -> set[str]:
     return set(
         chr(i) for i in range(sys.maxunicode) if unicodedata.category(chr(i)).startswith("P")
     )

fastembed/image/onnx_embedding.py

@@ -80,7 +80,7 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
                 Mutually exclusive with the `cuda` and `device_ids` arguments. Defaults to None.
             cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
                 Defaults to False.
-            device_ids (Optional[List[int]], optional): The list of device ids to use for data parallel processing in
+            device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
                 workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
             lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
                 Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
@@ -134,7 +134,7 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
         Lists the supported models.
 
         Returns:
-            List[Dict[str, Any]]: A list of dictionaries containing the model information.
+            list[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_onnx_models
 

tests/profiling.py

@@ -9,7 +9,7 @@
 
 # %%
 import time
-from typing import Callable, List, Tuple
+from typing import Callable
 
 import matplotlib.pyplot as plt
 import torch.nn.functional as F
@@ -23,7 +23,7 @@ from fastembed.embedding import DefaultEmbedding
 # data is a list of strings, each string is a document.
 
 # %%
-documents: List[str] = [
+documents: list[str] = [
     "Chandrayaan-3 is India's third lunar mission",
     "It aimed to land a rover on the Moon's surface - joining the US, China and Russia",
     "The mission is a follow-up to Chandrayaan-2, which had partial success",
@@ -56,7 +56,7 @@ class HF:
         self.model = AutoModel.from_pretrained(model_id)
         self.tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-    def embed(self, texts: List[str]):
+    def embed(self, texts: list[str]):
         encoded_input = self.tokenizer(
             texts, max_length=512, padding=True, truncation=True, return_tensors="pt"
         )
@@ -88,7 +88,7 @@ embedding_model = DefaultEmbedding()
 # %%
 def calculate_time_stats(
     embed_func: Callable, documents: list, k: int
-) -> Tuple[float, float, float]:
+) -> tuple[float, float, float]:
     times = []
     for _ in range(k):
         # Timing the embed_func call
@@ -111,8 +111,8 @@ print(f"FastEmbed (Average, Max, Min): {fst_stats}")
 
 # %%
 def plot_character_per_second_comparison(
-    hf_stats: Tuple[float, float, float],
-    fst_stats: Tuple[float, float, float],
+    hf_stats: tuple[float, float, float],
+    fst_stats: tuple[float, float, float],
     documents: list,
 ):
     # Calculating total characters in documents