fix links (#215)

README.md

@@ -2,7 +2,7 @@
 
 FastEmbed is a lightweight, fast, Python library built for embedding generation. We [support popular text models](https://qdrant.github.io/fastembed/examples/Supported_Models/). Please [open a GitHub issue](https://github.com/qdrant/fastembed/issues/new) if you want us to add a new model.
 
-The default text embedding (`TextEmbedding`) model is Flag Embedding, presented in the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. It supports "query" and "passage" prefixes for the input text. Here is an example for [Retrieval Embedding Generation](https://qdrant.github.io/fastembed/examples/Retrieval_with_FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/examples/Usage_With_Qdrant/).
+The default text embedding (`TextEmbedding`) model is Flag Embedding, presented in the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. It supports "query" and "passage" prefixes for the input text. Here is an example for [Retrieval Embedding Generation](https://qdrant.github.io/fastembed/qdrant/Retrieval_with_FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/qdrant/Usage_With_Qdrant/).
 
 ## 📈 Why FastEmbed?
 

fastembed/common/onnx_model.py

@@ -1,19 +1,7 @@
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import (
-    Any,
-    Dict,
-    Generic,
-    Iterable,
-    List,
-    Optional,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-    Sequence,
-)
+from typing import Any, Dict, Generic, Iterable, List, Optional, Tuple, Type, TypeVar, Union
 
 import numpy as np
 import onnxruntime as ort
@@ -51,21 +39,11 @@ class OnnxModel(Generic[T]):
         model_dir: Path,
         model_file: str,
         threads: Optional[int],
-        providers: Optional[Sequence[Union[str, Tuple[str, Dict[Any, Any]]]]] = None,
     ) -> None:
         model_path = model_dir / model_file
 
         # List of Execution Providers: https://onnxruntime.ai/docs/execution-providers
-
-        onnx_providers = ["CPUExecutionProvider"] if providers is None else list(providers)
-        available_providers = ort.get_available_providers()
-        for provider in onnx_providers:
-            # check providers available
-            provider_name = provider if isinstance(provider, str) else provider[0]
-            if provider_name not in available_providers:
-                raise ValueError(
-                    f"Provider {provider_name} is not available. Available providers: {available_providers}"
-                )
+        onnx_providers = ["CPUExecutionProvider"]
 
         so = ort.SessionOptions()
         so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL

pyproject.toml

@@ -12,16 +12,8 @@ keywords = ["vector", "embedding", "neural", "search", "qdrant", "sentence-trans
 
 [tool.poetry.dependencies]
 python = ">=3.8.0,<3.13"
-onnx = [
-    {version = "^1.15.0", optional = true, markers = "extra != 'gpu'"}
-]
-onnxruntime = [
-    {version = "^1.17.0", optional = true, markers = "extra != 'gpu'"}
-]
-onnxruntime-gpu = [
-    { version = "^1.17.0", optional = true, python = "<3.13", markers = "extra == 'gpu'" }
-]
-
+onnx = "^1.15.0"
+onnxruntime = "^1.17.0"
 tqdm = "^4.66"
 requests = "^2.31"
 tokenizers = "^0.15.1"
@@ -45,8 +37,6 @@ pillow = "^10.2.0"
 cairosvg = "^2.7.1"
 mknotebooks = "^0.8.0"
 
-[tool.poetry.extras]
-gpu = ["onnxruntime-gpu"]
 
 [build-system]
 requires = ["poetry-core"]