make format

model-providers/model_providers/_compat.py

@@ -1,11 +1,11 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, Union, Generic, TypeVar, Callable, cast, overload
 from datetime import date, datetime
-from typing_extensions import Self
+from typing import TYPE_CHECKING, Any, Callable, Generic, TypeVar, Union, cast, overload
 
 import pydantic
 from pydantic.fields import FieldInfo
+from typing_extensions import Self
 
 from ._types import StrBytesIntFloat
 
@@ -45,23 +45,49 @@ if TYPE_CHECKING:
 
 else:
     if PYDANTIC_V2:
+        from pydantic.v1.datetime_parse import (
+            parse_date as parse_date,
+        )
+        from pydantic.v1.datetime_parse import (
+            parse_datetime as parse_datetime,
+        )
         from pydantic.v1.typing import (
             get_args as get_args,
-            is_union as is_union,
+        )
+        from pydantic.v1.typing import (
             get_origin as get_origin,
-            is_typeddict as is_typeddict,
+        )
+        from pydantic.v1.typing import (
             is_literal_type as is_literal_type,
         )
-        from pydantic.v1.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime
+        from pydantic.v1.typing import (
+            is_typeddict as is_typeddict,
+        )
+        from pydantic.v1.typing import (
+            is_union as is_union,
+        )
     else:
+        from pydantic.datetime_parse import (
+            parse_date as parse_date,
+        )
+        from pydantic.datetime_parse import (
+            parse_datetime as parse_datetime,
+        )
         from pydantic.typing import (
             get_args as get_args,
-            is_union as is_union,
+        )
+        from pydantic.typing import (
             get_origin as get_origin,
-            is_typeddict as is_typeddict,
+        )
+        from pydantic.typing import (
             is_literal_type as is_literal_type,
         )
-        from pydantic.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime
+        from pydantic.typing import (
+            is_typeddict as is_typeddict,
+        )
+        from pydantic.typing import (
+            is_union as is_union,
+        )
 
 
 # refactored config
@@ -204,7 +230,9 @@ if TYPE_CHECKING:
         def __get__(self, instance: object, owner: type[Any] | None = None) -> _T:
             ...
 
-        def __get__(self, instance: object, owner: type[Any] | None = None) -> _T | Self:
+        def __get__(
+            self, instance: object, owner: type[Any] | None = None
+        ) -> _T | Self:
             raise NotImplementedError()
 
         def __set_name__(self, owner: type[Any], name: str) -> None:

model-providers/model_providers/_files.py

@@ -4,20 +4,20 @@ import io
 import os
 import pathlib
 from typing import overload
-from typing_extensions import TypeGuard
 
 import anyio
+from typing_extensions import TypeGuard
 
 from ._types import (
-    FileTypes,
-    FileContent,
-    RequestFiles,
-    HttpxFileTypes,
     Base64FileInput,
+    FileContent,
+    FileTypes,
     HttpxFileContent,
+    HttpxFileTypes,
     HttpxRequestFiles,
+    RequestFiles,
 )
-from ._utils import is_tuple_t, is_mapping_t, is_sequence_t
+from ._utils import is_mapping_t, is_sequence_t, is_tuple_t
 
 
 def is_base64_file_input(obj: object) -> TypeGuard[Base64FileInput]:
@@ -26,13 +26,20 @@ def is_base64_file_input(obj: object) -> TypeGuard[Base64FileInput]:
 
 def is_file_content(obj: object) -> TypeGuard[FileContent]:
     return (
-        isinstance(obj, bytes) or isinstance(obj, tuple) or isinstance(obj, io.IOBase) or isinstance(obj, os.PathLike)
+        isinstance(obj, bytes)
+        or isinstance(obj, tuple)
+        or isinstance(obj, io.IOBase)
+        or isinstance(obj, os.PathLike)
     )
 
 
 def assert_is_file_content(obj: object, *, key: str | None = None) -> None:
     if not is_file_content(obj):
-        prefix = f"Expected entry at `{key}`" if key is not None else f"Expected file input `{obj!r}`"
+        prefix = (
+            f"Expected entry at `{key}`"
+            if key is not None
+            else f"Expected file input `{obj!r}`"
+        )
         raise RuntimeError(
             f"{prefix} to be bytes, an io.IOBase instance, PathLike or a tuple but received {type(obj)} instead. See https://github.com/openai/openai-python/tree/main#file-uploads"
         ) from None
@@ -57,7 +64,9 @@ def to_httpx_files(files: RequestFiles | None) -> HttpxRequestFiles | None:
     elif is_sequence_t(files):
         files = [(key, _transform_file(file)) for key, file in files]
     else:
-        raise TypeError(f"Unexpected file type input {type(files)}, expected mapping or sequence")
+        raise TypeError(
+            f"Unexpected file type input {type(files)}, expected mapping or sequence"
+        )
 
     return files
 
@@ -73,7 +82,9 @@ def _transform_file(file: FileTypes) -> HttpxFileTypes:
     if is_tuple_t(file):
         return (file[0], _read_file_content(file[1]), *file[2:])
 
-    raise TypeError(f"Expected file types input to be a FileContent type or to be a tuple")
+    raise TypeError(
+        f"Expected file types input to be a FileContent type or to be a tuple"
+    )
 
 
 def _read_file_content(file: FileContent) -> HttpxFileContent:
@@ -101,7 +112,9 @@ async def async_to_httpx_files(files: RequestFiles | None) -> HttpxRequestFiles
     elif is_sequence_t(files):
         files = [(key, await _async_transform_file(file)) for key, file in files]
     else:
-        raise TypeError("Unexpected file type input {type(files)}, expected mapping or sequence")
+        raise TypeError(
+            "Unexpected file type input {type(files)}, expected mapping or sequence"
+        )
 
     return files
 
@@ -117,7 +130,9 @@ async def _async_transform_file(file: FileTypes) -> HttpxFileTypes:
     if is_tuple_t(file):
         return (file[0], await _async_read_file_content(file[1]), *file[2:])
 
-    raise TypeError(f"Expected file types input to be a FileContent type or to be a tuple")
+    raise TypeError(
+        f"Expected file types input to be a FileContent type or to be a tuple"
+    )
 
 
 async def _async_read_file_content(file: FileContent) -> HttpxFileContent:

model-providers/model_providers/_models.py

@@ -1,60 +1,62 @@
 from __future__ import annotations
 
-import os
 import inspect
-from typing import TYPE_CHECKING, Any, Type, Union, Generic, TypeVar, Callable, cast
+import os
 from datetime import date, datetime
+from typing import TYPE_CHECKING, Any, Callable, Generic, Type, TypeVar, Union, cast
+
+import pydantic
+import pydantic.generics
+from pydantic.fields import FieldInfo
 from typing_extensions import (
-    Unpack,
-    Literal,
     ClassVar,
+    Literal,
     Protocol,
     Required,
     TypedDict,
     TypeGuard,
+    Unpack,
     final,
     override,
     runtime_checkable,
 )
 
-import pydantic
+from ._compat import (
-import pydantic.generics
+    PYDANTIC_V2,
-from pydantic.fields import FieldInfo
+    ConfigDict,
-
+    field_get_default,
+    get_args,
+    get_model_config,
+    get_model_fields,
+    get_origin,
+    is_literal_type,
+    is_union,
+    parse_obj,
+)
+from ._compat import (
+    GenericModel as BaseGenericModel,
+)
 from ._types import (
     IncEx,
     ModelT,
 )
 from ._utils import (
     PropertyInfo,
-    is_list,
-    is_given,
-    lru_cache,
-    is_mapping,
-    parse_date,
     coerce_boolean,
-    parse_datetime,
-    strip_not_given,
     extract_type_arg,
     is_annotated_type,
+    is_given,
+    is_list,
+    is_mapping,
+    lru_cache,
+    parse_date,
+    parse_datetime,
     strip_annotated_type,
-)
+    strip_not_given,
-from ._compat import (
-    PYDANTIC_V2,
-    ConfigDict,
-    GenericModel as BaseGenericModel,
-    get_args,
-    is_union,
-    parse_obj,
-    get_origin,
-    is_literal_type,
-    get_model_config,
-    get_model_fields,
-    field_get_default,
 )
 
 if TYPE_CHECKING:
-    from pydantic_core.core_schema import ModelField, LiteralSchema, ModelFieldsSchema
+    from pydantic_core.core_schema import LiteralSchema, ModelField, ModelFieldsSchema
 
 __all__ = ["BaseModel", "GenericModel"]
 
@@ -69,7 +71,8 @@ class _ConfigProtocol(Protocol):
 class BaseModel(pydantic.BaseModel):
     if PYDANTIC_V2:
         model_config: ClassVar[ConfigDict] = ConfigDict(
-            extra="allow", defer_build=coerce_boolean(os.environ.get("DEFER_PYDANTIC_BUILD", "true"))
+            extra="allow",
+            defer_build=coerce_boolean(os.environ.get("DEFER_PYDANTIC_BUILD", "true")),
         )
     else:
 
@@ -189,7 +192,9 @@ class BaseModel(pydantic.BaseModel):
                 key = name
 
             if key in values:
-                fields_values[name] = _construct_field(value=values[key], field=field, key=key)
+                fields_values[name] = _construct_field(
+                    value=values[key], field=field, key=key
+                )
                 _fields_set.add(name)
             else:
                 fields_values[name] = field_get_default(field)
@@ -412,9 +417,13 @@ def construct_type(*, value: object, type_: object) -> object:
         #
         # without this block, if the data we get is something like `{'kind': 'bar', 'value': 'foo'}` then
         # we'd end up constructing `FooType` when it should be `BarType`.
-        discriminator = _build_discriminated_union_meta(union=type_, meta_annotations=meta)
+        discriminator = _build_discriminated_union_meta(
+            union=type_, meta_annotations=meta
+        )
         if discriminator and is_mapping(value):
-            variant_value = value.get(discriminator.field_alias_from or discriminator.field_name)
+            variant_value = value.get(
+                discriminator.field_alias_from or discriminator.field_name
+            )
             if variant_value and isinstance(variant_value, str):
                 variant_type = discriminator.mapping.get(variant_value)
                 if variant_type:
@@ -434,11 +443,19 @@ def construct_type(*, value: object, type_: object) -> object:
             return value
 
         _, items_type = get_args(type_)  # Dict[_, items_type]
-        return {key: construct_type(value=item, type_=items_type) for key, item in value.items()}
+        return {
+            key: construct_type(value=item, type_=items_type)
+            for key, item in value.items()
+        }
 
-    if not is_literal_type(type_) and (issubclass(origin, BaseModel) or issubclass(origin, GenericModel)):
+    if not is_literal_type(type_) and (
+        issubclass(origin, BaseModel) or issubclass(origin, GenericModel)
+    ):
         if is_list(value):
-            return [cast(Any, type_).construct(**entry) if is_mapping(entry) else entry for entry in value]
+            return [
+                cast(Any, type_).construct(**entry) if is_mapping(entry) else entry
+                for entry in value
+            ]
 
         if is_mapping(value):
             if issubclass(type_, BaseModel):
@@ -523,14 +540,19 @@ class DiscriminatorDetails:
         self.field_alias_from = discriminator_alias
 
 
-def _build_discriminated_union_meta(*, union: type, meta_annotations: tuple[Any, ...]) -> DiscriminatorDetails | None:
+def _build_discriminated_union_meta(
+    *, union: type, meta_annotations: tuple[Any, ...]
+) -> DiscriminatorDetails | None:
     if isinstance(union, CachedDiscriminatorType):
         return union.__discriminator__
 
     discriminator_field_name: str | None = None
 
     for annotation in meta_annotations:
-        if isinstance(annotation, PropertyInfo) and annotation.discriminator is not None:
+        if (
+            isinstance(annotation, PropertyInfo)
+            and annotation.discriminator is not None
+        ):
             discriminator_field_name = annotation.discriminator
             break
 
@@ -558,7 +580,9 @@ def _build_discriminated_union_meta(*, union: type, meta_annotations: tuple[Any,
                         if isinstance(entry, str):
                             mapping[entry] = variant
             else:
-                field_info = cast("dict[str, FieldInfo]", variant.__fields__).get(discriminator_field_name)  # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
+                field_info = cast("dict[str, FieldInfo]", variant.__fields__).get(
+                    discriminator_field_name
+                )  # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
                 if not field_info:
                     continue
 
@@ -582,7 +606,9 @@ def _build_discriminated_union_meta(*, union: type, meta_annotations: tuple[Any,
     return details
 
 
-def _extract_field_schema_pv2(model: type[BaseModel], field_name: str) -> ModelField | None:
+def _extract_field_schema_pv2(
+    model: type[BaseModel], field_name: str
+) -> ModelField | None:
     schema = model.__pydantic_core_schema__
     if schema["type"] != "model":
         return None
@@ -621,7 +647,9 @@ else:
 if PYDANTIC_V2:
     from pydantic import TypeAdapter as _TypeAdapter
 
-    _CachedTypeAdapter = cast("TypeAdapter[object]", lru_cache(maxsize=None)(_TypeAdapter))
+    _CachedTypeAdapter = cast(
+        "TypeAdapter[object]", lru_cache(maxsize=None)(_TypeAdapter)
+    )
 
     if TYPE_CHECKING:
         from pydantic import TypeAdapter
@@ -652,6 +680,3 @@ elif not TYPE_CHECKING:  # TODO: condition is weird
 
     def _create_pydantic_model(type_: _T) -> Type[RootModel[_T]]:
         return RootModel[type_]  # type: ignore
-
-
-

model-providers/model_providers/_types.py

@@ -5,22 +5,29 @@ from typing import (
     IO,
     TYPE_CHECKING,
     Any,
+    Callable,
     Dict,
     List,
-    Type,
-    Tuple,
-    Union,
     Mapping,
-    TypeVar,
-    Callable,
     Optional,
     Sequence,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
 )
-from typing_extensions import Literal, Protocol, TypeAlias, TypedDict, override, runtime_checkable
 
 import httpx
 import pydantic
-from httpx import URL, Proxy, Timeout, Response, BaseTransport, AsyncBaseTransport
+from httpx import URL, AsyncBaseTransport, BaseTransport, Proxy, Response, Timeout
+from typing_extensions import (
+    Literal,
+    Protocol,
+    TypeAlias,
+    TypedDict,
+    override,
+    runtime_checkable,
+)
 
 if TYPE_CHECKING:
     from ._models import BaseModel
@@ -43,7 +50,9 @@ if TYPE_CHECKING:
     FileContent = Union[IO[bytes], bytes, PathLike[str]]
 else:
     Base64FileInput = Union[IO[bytes], PathLike]
-    FileContent = Union[IO[bytes], bytes, PathLike]  # PathLike is not subscriptable in Python 3.8.
+    FileContent = Union[
+        IO[bytes], bytes, PathLike
+    ]  # PathLike is not subscriptable in Python 3.8.
 FileTypes = Union[
     # file (or bytes)
     FileContent,
@@ -68,7 +77,9 @@ HttpxFileTypes = Union[
     # (filename, file (or bytes), content_type, headers)
     Tuple[Optional[str], HttpxFileContent, Optional[str], Mapping[str, str]],
 ]
-HttpxRequestFiles = Union[Mapping[str, HttpxFileTypes], Sequence[Tuple[str, HttpxFileTypes]]]
+HttpxRequestFiles = Union[
+    Mapping[str, HttpxFileTypes], Sequence[Tuple[str, HttpxFileTypes]]
+]
 
 # Workaround to support (cast_to: Type[ResponseT]) -> ResponseT
 # where ResponseT includes `None`. In order to support directly

model-providers/model_providers/_utils/__init__.py

@@ -1,48 +1,126 @@
-from ._utils import (
+from ._transform import (
-    flatten as flatten,
+    PropertyInfo as PropertyInfo,
-    is_dict as is_dict,
+)
-    is_list as is_list,
+from ._transform import (
-    is_given as is_given,
+    async_maybe_transform as async_maybe_transform,
-    is_tuple as is_tuple,
+)
-    lru_cache as lru_cache,
+from ._transform import (
-    is_mapping as is_mapping,
+    async_transform as async_transform,
-    is_tuple_t as is_tuple_t,
+)
-    parse_date as parse_date,
+from ._transform import (
-    is_iterable as is_iterable,
+    maybe_transform as maybe_transform,
-    is_sequence as is_sequence,
+)
-    coerce_float as coerce_float,
+from ._transform import (
-    is_mapping_t as is_mapping_t,
+    transform as transform,
-    removeprefix as removeprefix,
+)
-    removesuffix as removesuffix,
+from ._typing import (
-    extract_files as extract_files,
+    extract_type_arg as extract_type_arg,
-    is_sequence_t as is_sequence_t,
+)
-    required_args as required_args,
+from ._typing import (
-    coerce_boolean as coerce_boolean,
+    extract_type_var_from_base as extract_type_var_from_base,
-    coerce_integer as coerce_integer,
+)
-    file_from_path as file_from_path,
+from ._typing import (
-    parse_datetime as parse_datetime,
+    is_annotated_type as is_annotated_type,
-    strip_not_given as strip_not_given,
+)
-    deepcopy_minimal as deepcopy_minimal,
+from ._typing import (
-    get_async_library as get_async_library,
+    is_iterable_type as is_iterable_type,
-    maybe_coerce_float as maybe_coerce_float,
-    get_required_header as get_required_header,
-    maybe_coerce_boolean as maybe_coerce_boolean,
-    maybe_coerce_integer as maybe_coerce_integer,
 )
 from ._typing import (
     is_list_type as is_list_type,
-    is_union_type as is_union_type,
+)
-    extract_type_arg as extract_type_arg,
+from ._typing import (
-    is_iterable_type as is_iterable_type,
     is_required_type as is_required_type,
-    is_annotated_type as is_annotated_type,
+)
+from ._typing import (
+    is_union_type as is_union_type,
+)
+from ._typing import (
     strip_annotated_type as strip_annotated_type,
-    extract_type_var_from_base as extract_type_var_from_base,
 )
-from ._transform import (
+from ._utils import (
-    PropertyInfo as PropertyInfo,
+    coerce_boolean as coerce_boolean,
-    transform as transform,
+)
-    async_transform as async_transform,
+from ._utils import (
-    maybe_transform as maybe_transform,
+    coerce_float as coerce_float,
-    async_maybe_transform as async_maybe_transform,
+)
+from ._utils import (
+    coerce_integer as coerce_integer,
+)
+from ._utils import (
+    deepcopy_minimal as deepcopy_minimal,
+)
+from ._utils import (
+    extract_files as extract_files,
+)
+from ._utils import (
+    file_from_path as file_from_path,
+)
+from ._utils import (
+    flatten as flatten,
+)
+from ._utils import (
+    get_async_library as get_async_library,
+)
+from ._utils import (
+    get_required_header as get_required_header,
+)
+from ._utils import (
+    is_dict as is_dict,
+)
+from ._utils import (
+    is_given as is_given,
+)
+from ._utils import (
+    is_iterable as is_iterable,
+)
+from ._utils import (
+    is_list as is_list,
+)
+from ._utils import (
+    is_mapping as is_mapping,
+)
+from ._utils import (
+    is_mapping_t as is_mapping_t,
+)
+from ._utils import (
+    is_sequence as is_sequence,
+)
+from ._utils import (
+    is_sequence_t as is_sequence_t,
+)
+from ._utils import (
+    is_tuple as is_tuple,
+)
+from ._utils import (
+    is_tuple_t as is_tuple_t,
+)
+from ._utils import (
+    lru_cache as lru_cache,
+)
+from ._utils import (
+    maybe_coerce_boolean as maybe_coerce_boolean,
+)
+from ._utils import (
+    maybe_coerce_float as maybe_coerce_float,
+)
+from ._utils import (
+    maybe_coerce_integer as maybe_coerce_integer,
+)
+from ._utils import (
+    parse_date as parse_date,
+)
+from ._utils import (
+    parse_datetime as parse_datetime,
+)
+from ._utils import (
+    removeprefix as removeprefix,
+)
+from ._utils import (
+    removesuffix as removesuffix,
+)
+from ._utils import (
+    required_args as required_args,
+)
+from ._utils import (
+    strip_not_given as strip_not_given,
 )

model-providers/model_providers/_utils/_transform.py

@@ -1,31 +1,31 @@
 from __future__ import annotations
 
-import io
 import base64
+import io
 import pathlib
-from typing import Any, Mapping, TypeVar, cast
 from datetime import date, datetime
-from typing_extensions import Literal, get_args, override, get_type_hints
+from typing import Any, Mapping, TypeVar, cast
 
 import anyio
 import pydantic
+from typing_extensions import Literal, get_args, get_type_hints, override
 
-from ._utils import (
+from .._compat import is_typeddict, model_dump
-    is_list,
-    is_mapping,
-    is_iterable,
-)
 from .._files import is_base64_file_input
 from ._typing import (
-    is_list_type,
-    is_union_type,
     extract_type_arg,
-    is_iterable_type,
-    is_required_type,
     is_annotated_type,
+    is_iterable_type,
+    is_list_type,
+    is_required_type,
+    is_union_type,
     strip_annotated_type,
 )
-from .._compat import model_dump, is_typeddict
+from ._utils import (
+    is_iterable,
+    is_list,
+    is_mapping,
+)
 
 _T = TypeVar("_T")
 
@@ -171,10 +171,17 @@ def _transform_recursive(
         # List[T]
         (is_list_type(stripped_type) and is_list(data))
         # Iterable[T]
-        or (is_iterable_type(stripped_type) and is_iterable(data) and not isinstance(data, str))
+        or (
+            is_iterable_type(stripped_type)
+            and is_iterable(data)
+            and not isinstance(data, str)
+        )
     ):
         inner_type = extract_type_arg(stripped_type, 0)
-        return [_transform_recursive(d, annotation=annotation, inner_type=inner_type) for d in data]
+        return [
+            _transform_recursive(d, annotation=annotation, inner_type=inner_type)
+            for d in data
+        ]
 
     if is_union_type(stripped_type):
         # For union types we run the transformation against all subtypes to ensure that everything is transformed.
@@ -201,7 +208,9 @@ def _transform_recursive(
     return data
 
 
-def _format_data(data: object, format_: PropertyFormat, format_template: str | None) -> object:
+def _format_data(
+    data: object, format_: PropertyFormat, format_template: str | None
+) -> object:
     if isinstance(data, (date, datetime)):
         if format_ == "iso8601":
             return data.isoformat()
@@ -221,7 +230,9 @@ def _format_data(data: object, format_: PropertyFormat, format_template: str | N
                 binary = binary.encode()
 
         if not isinstance(binary, bytes):
-            raise RuntimeError(f"Could not read bytes from {data}; Received {type(binary)}")
+            raise RuntimeError(
+                f"Could not read bytes from {data}; Received {type(binary)}"
+            )
 
         return base64.b64encode(binary).decode("ascii")
 
@@ -240,7 +251,9 @@ def _transform_typeddict(
             # we do not have a type annotation for this field, leave it as is
             result[key] = value
         else:
-            result[_maybe_transform_key(key, type_)] = _transform_recursive(value, annotation=type_)
+            result[_maybe_transform_key(key, type_)] = _transform_recursive(
+                value, annotation=type_
+            )
     return result
 
 
@@ -276,7 +289,9 @@ async def async_transform(
 
     It should be noted that the transformations that this function does are not represented in the type system.
     """
-    transformed = await _async_transform_recursive(data, annotation=cast(type, expected_type))
+    transformed = await _async_transform_recursive(
+        data, annotation=cast(type, expected_type)
+    )
     return cast(_T, transformed)
 
 
@@ -309,10 +324,19 @@ async def _async_transform_recursive(
         # List[T]
         (is_list_type(stripped_type) and is_list(data))
         # Iterable[T]
-        or (is_iterable_type(stripped_type) and is_iterable(data) and not isinstance(data, str))
+        or (
+            is_iterable_type(stripped_type)
+            and is_iterable(data)
+            and not isinstance(data, str)
+        )
     ):
         inner_type = extract_type_arg(stripped_type, 0)
-        return [await _async_transform_recursive(d, annotation=annotation, inner_type=inner_type) for d in data]
+        return [
+            await _async_transform_recursive(
+                d, annotation=annotation, inner_type=inner_type
+            )
+            for d in data
+        ]
 
     if is_union_type(stripped_type):
         # For union types we run the transformation against all subtypes to ensure that everything is transformed.
@@ -320,7 +344,9 @@ async def _async_transform_recursive(
         # TODO: there may be edge cases where the same normalized field name will transform to two different names
         # in different subtypes.
         for subtype in get_args(stripped_type):
-            data = await _async_transform_recursive(data, annotation=annotation, inner_type=subtype)
+            data = await _async_transform_recursive(
+                data, annotation=annotation, inner_type=subtype
+            )
         return data
 
     if isinstance(data, pydantic.BaseModel):
@@ -334,12 +360,16 @@ async def _async_transform_recursive(
     annotations = get_args(annotated_type)[1:]
     for annotation in annotations:
         if isinstance(annotation, PropertyInfo) and annotation.format is not None:
-            return await _async_format_data(data, annotation.format, annotation.format_template)
+            return await _async_format_data(
+                data, annotation.format, annotation.format_template
+            )
 
     return data
 
 
-async def _async_format_data(data: object, format_: PropertyFormat, format_template: str | None) -> object:
+async def _async_format_data(
+    data: object, format_: PropertyFormat, format_template: str | None
+) -> object:
     if isinstance(data, (date, datetime)):
         if format_ == "iso8601":
             return data.isoformat()
@@ -359,7 +389,9 @@ async def _async_format_data(data: object, format_: PropertyFormat, format_templ
                 binary = binary.encode()
 
         if not isinstance(binary, bytes):
-            raise RuntimeError(f"Could not read bytes from {data}; Received {type(binary)}")
+            raise RuntimeError(
+                f"Could not read bytes from {data}; Received {type(binary)}"
+            )
 
         return base64.b64encode(binary).decode("ascii")
 
@@ -378,5 +410,7 @@ async def _async_transform_typeddict(
             # we do not have a type annotation for this field, leave it as is
             result[key] = value
         else:
-            result[_maybe_transform_key(key, type_)] = await _async_transform_recursive(value, annotation=type_)
+            result[_maybe_transform_key(key, type_)] = await _async_transform_recursive(
+                value, annotation=type_
+            )
     return result

model-providers/model_providers/_utils/_typing.py

@@ -1,11 +1,12 @@
 from __future__ import annotations
 
-from typing import Any, TypeVar, Iterable, cast
 from collections import abc as _c_abc
-from typing_extensions import Required, Annotated, get_args, get_origin
+from typing import Any, Iterable, TypeVar, cast
+
+from typing_extensions import Annotated, Required, get_args, get_origin
 
-from .._types import InheritsGeneric
 from .._compat import is_union as _is_union
+from .._types import InheritsGeneric
 
 
 def is_annotated_type(typ: type) -> bool:
@@ -49,15 +50,17 @@ def extract_type_arg(typ: type, index: int) -> type:
     try:
         return cast(type, args[index])
     except IndexError as err:
-        raise RuntimeError(f"Expected type {typ} to have a type argument at index {index} but it did not") from err
+        raise RuntimeError(
+            f"Expected type {typ} to have a type argument at index {index} but it did not"
+        ) from err
 
 
 def extract_type_var_from_base(
-        typ: type,
+    typ: type,
-        *,
+    *,
-        generic_bases: tuple[type, ...],
+    generic_bases: tuple[type, ...],
-        index: int,
+    index: int,
-        failure_message: str | None = None,
+    failure_message: str | None = None,
 ) -> type:
     """Given a type like `Foo[T]`, returns the generic type variable `T`.
 
@@ -117,4 +120,7 @@ def extract_type_var_from_base(
 
         return extracted
 
-    raise RuntimeError(failure_message or f"Could not resolve inner type variable at index {index} for {typ}")
+    raise RuntimeError(
+        failure_message
+        or f"Could not resolve inner type variable at index {index} for {typ}"
+    )

model-providers/model_providers/_utils/_utils.py

@@ -1,27 +1,28 @@
 from __future__ import annotations
 
+import functools
+import inspect
 import os
 import re
-import inspect
+from pathlib import Path
-import functools
 from typing import (
     Any,
-    Tuple,
-    Mapping,
-    TypeVar,
     Callable,
     Iterable,
+    Mapping,
     Sequence,
+    Tuple,
+    TypeVar,
     cast,
     overload,
 )
-from pathlib import Path
-from typing_extensions import TypeGuard
 
 import sniffio
+from typing_extensions import TypeGuard
 
-from .._types import Headers, NotGiven, FileTypes, NotGivenOr, HeadersLike
+from .._compat import parse_date as parse_date
-from .._compat import parse_date as parse_date, parse_datetime as parse_datetime
+from .._compat import parse_datetime as parse_datetime
+from .._types import FileTypes, Headers, HeadersLike, NotGiven, NotGivenOr
 
 _T = TypeVar("_T")
 _TupleT = TypeVar("_TupleT", bound=Tuple[object, ...])
@@ -108,7 +109,9 @@ def _extract_items(
                     item,
                     path,
                     index=index,
-                    flattened_key=flattened_key + "[]" if flattened_key is not None else "[]",
+                    flattened_key=flattened_key + "[]"
+                    if flattened_key is not None
+                    else "[]",
                 )
                 for item in obj
             ]
@@ -261,7 +264,12 @@ def required_args(*variants: Sequence[str]) -> Callable[[CallableT], CallableT]:
             else:  # no break
                 if len(variants) > 1:
                     variations = human_join(
-                        ["(" + human_join([quote(arg) for arg in variant], final="and") + ")" for variant in variants]
+                        [
+                            "("
+                            + human_join([quote(arg) for arg in variant], final="and")
+                            + ")"
+                            for variant in variants
+                        ]
                     )
                     msg = f"Missing required arguments; Expected either {variations} arguments to be given"
                 else:
@@ -376,7 +384,11 @@ def get_required_header(headers: HeadersLike, header: str) -> str:
                 return v
 
     """ to deal with the case where the header looks like Stainless-Event-Id """
-    intercaps_header = re.sub(r"([^\w])(\w)", lambda pat: pat.group(1) + pat.group(2).upper(), header.capitalize())
+    intercaps_header = re.sub(
+        r"([^\w])(\w)",
+        lambda pat: pat.group(1) + pat.group(2).upper(),
+        header.capitalize(),
+    )
 
     for normalized_header in [header, lower_header, header.upper(), intercaps_header]:
         value = headers.get(normalized_header)

model-providers/model_providers/bootstrap_web/entities/model_provider_entities.py

@@ -1,9 +1,6 @@
 from enum import Enum
 from typing import List, Literal, Optional
 
-from ..._compat import PYDANTIC_V2, ConfigDict
-from ..._models import BaseModel
-
 from model_providers.core.entities.model_entities import (
     ModelStatus,
     ModelWithProviderEntity,
@@ -26,6 +23,9 @@ from model_providers.core.model_runtime.entities.provider_entities import (
     SimpleProviderEntity,
 )
 
+from ..._compat import PYDANTIC_V2, ConfigDict
+from ..._models import BaseModel
+
 
 class CustomConfigurationStatus(Enum):
     """
@@ -74,10 +74,9 @@ class ProviderResponse(BaseModel):
     custom_configuration: CustomConfigurationResponse
     system_configuration: SystemConfigurationResponse
     if PYDANTIC_V2:
-        model_config = ConfigDict(
+        model_config = ConfigDict(protected_namespaces=())
-            protected_namespaces=()
-        )
     else:
+
         class Config:
             protected_namespaces = ()
 
@@ -180,10 +179,9 @@ class DefaultModelResponse(BaseModel):
     provider: SimpleProviderEntityResponse
 
     if PYDANTIC_V2:
-        model_config = ConfigDict(
+        model_config = ConfigDict(protected_namespaces=())
-            protected_namespaces=()
-        )
     else:
+
         class Config:
             protected_namespaces = ()
 

model-providers/model_providers/bootstrap_web/openai_bootstrap_web.py

@@ -73,7 +73,7 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
         self._server = None
         self._server_thread = None
 
-    def logging_conf(self,logging_conf: Optional[dict] = None):
+    def logging_conf(self, logging_conf: Optional[dict] = None):
         self._logging_conf = logging_conf
 
     @classmethod
@@ -132,7 +132,10 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
         self._app.include_router(self._router)
 
         config = Config(
-            app=self._app, host=self._host, port=self._port, log_config=self._logging_conf
+            app=self._app,
+            host=self._host,
+            port=self._port,
+            log_config=self._logging_conf,
         )
         self._server = Server(config)
 
@@ -145,7 +148,6 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
         self._server_thread.start()
 
     def destroy(self):
-
         logger.info("Shutting down server")
         self._server.should_exit = True  # 设置退出标志
         self._server.shutdown()  # 停止服务器
@@ -155,7 +157,6 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
     def join(self):
         self._server_thread.join()
 
-
     def set_app_event(self, started_event: mp.Event = None):
         @self._app.on_event("startup")
         async def on_startup():
@@ -190,12 +191,15 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
                     provider_model_bundle.model_type_instance.predefined_models()
                 )
                 # 获取自定义模型
-                for model in provider_model_bundle.configuration.custom_configuration.models:
+                for (
-
+                    model
-                    llm_models.append(provider_model_bundle.model_type_instance.get_model_schema(
+                ) in provider_model_bundle.configuration.custom_configuration.models:
-                        model=model.model,
+                    llm_models.append(
-                        credentials=model.credentials,
+                        provider_model_bundle.model_type_instance.get_model_schema(
-                    ))
+                            model=model.model,
+                            credentials=model.credentials,
+                        )
+                    )
             except Exception as e:
                 logger.error(
                     f"Error while fetching models for provider: {provider}, model_type: {model_type}"
@@ -225,13 +229,15 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
             )
 
             # 判断embeddings_request.input是否为list
-            input = ''
+            input = ""
             if isinstance(embeddings_request.input, list):
                 tokens = embeddings_request.input
                 try:
                     encoding = tiktoken.encoding_for_model(embeddings_request.model)
                 except KeyError:
-                    logger.warning("Warning: model not found. Using cl100k_base encoding.")
+                    logger.warning(
+                        "Warning: model not found. Using cl100k_base encoding."
+                    )
                     model = "cl100k_base"
                     encoding = tiktoken.get_encoding(model)
                 for i, token in enumerate(tokens):
@@ -241,7 +247,9 @@ class RESTFulOpenAIBootstrapBaseWeb(OpenAIBootstrapBaseWeb):
             else:
                 input = embeddings_request.input
 
-            response = model_instance.invoke_text_embedding(texts=[input], user="abc-123")
+            response = model_instance.invoke_text_embedding(
+                texts=[input], user="abc-123"
+            )
             return await openai_embedding_text(response)
 
         except ValueError as e:

model-providers/model_providers/core/bootstrap/openai_protocol.py

@@ -1,10 +1,12 @@
 import time
 from enum import Enum
 from typing import Any, Dict, List, Optional, Union
-from ..._models import BaseModel
+
 from pydantic import Field as FieldInfo
 from typing_extensions import Literal
 
+from ..._models import BaseModel
+
 
 class Role(str, Enum):
     USER = "user"

model-providers/model_providers/core/entities/message_entities.py

@@ -1,5 +1,5 @@
 import enum
-from typing import Any, cast, List
+from typing import Any, List, cast
 
 from langchain.schema import (
     AIMessage,
@@ -8,7 +8,6 @@ from langchain.schema import (
     HumanMessage,
     SystemMessage,
 )
-from ..._models import BaseModel
 
 from model_providers.core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -20,6 +19,8 @@ from model_providers.core.model_runtime.entities.message_entities import (
     UserPromptMessage,
 )
 
+from ..._models import BaseModel
+
 
 class PromptMessageFileType(enum.Enum):
     IMAGE = "image"

model-providers/model_providers/core/entities/model_entities.py

@@ -1,9 +1,6 @@
 from enum import Enum
 from typing import List, Optional
 
-from ..._compat import PYDANTIC_V2, ConfigDict
-from ..._models import BaseModel
-
 from model_providers.core.model_runtime.entities.common_entities import I18nObject
 from model_providers.core.model_runtime.entities.model_entities import (
     ModelType,
@@ -11,6 +8,9 @@ from model_providers.core.model_runtime.entities.model_entities import (
 )
 from model_providers.core.model_runtime.entities.provider_entities import ProviderEntity
 
+from ..._compat import PYDANTIC_V2, ConfigDict
+from ..._models import BaseModel
+
 
 class ModelStatus(Enum):
     """
@@ -80,9 +80,8 @@ class DefaultModelEntity(BaseModel):
     provider: DefaultModelProviderEntity
 
     if PYDANTIC_V2:
-        model_config = ConfigDict(
+        model_config = ConfigDict(protected_namespaces=())
-            protected_namespaces=()
-        )
     else:
+
         class Config:
-            protected_namespaces = ()
+            protected_namespaces = ()

model-providers/model_providers/core/entities/provider_configuration.py

@@ -4,9 +4,6 @@ import logging
 from json import JSONDecodeError
 from typing import Dict, Iterator, List, Optional
 
-from ..._compat import PYDANTIC_V2, ConfigDict
-from ..._models import BaseModel
-
 from model_providers.core.entities.model_entities import (
     ModelStatus,
     ModelWithProviderEntity,
@@ -29,6 +26,9 @@ from model_providers.core.model_runtime.model_providers.__base.model_provider im
     ModelProvider,
 )
 
+from ..._compat import PYDANTIC_V2, ConfigDict
+from ..._models import BaseModel
+
 logger = logging.getLogger(__name__)
 
 
@@ -351,11 +351,9 @@ class ProviderModelBundle(BaseModel):
     model_type_instance: AIModel
 
     if PYDANTIC_V2:
-        model_config = ConfigDict(
+        model_config = ConfigDict(protected_namespaces=(), arbitrary_types_allowed=True)
-            protected_namespaces=(),
-            arbitrary_types_allowed=True
-        )
     else:
+
         class Config:
             protected_namespaces = ()
 

model-providers/model_providers/core/entities/provider_entities.py

@@ -1,11 +1,11 @@
 from enum import Enum
 from typing import List, Optional
 
+from model_providers.core.model_runtime.entities.model_entities import ModelType
+
 from ..._compat import PYDANTIC_V2, ConfigDict
 from ..._models import BaseModel
 
-from model_providers.core.model_runtime.entities.model_entities import ModelType
-
 
 class ProviderType(Enum):
     CUSTOM = "custom"
@@ -59,10 +59,9 @@ class RestrictModel(BaseModel):
     model_type: ModelType
 
     if PYDANTIC_V2:
-        model_config = ConfigDict(
+        model_config = ConfigDict(protected_namespaces=())
-            protected_namespaces=()
-        )
     else:
+
         class Config:
             protected_namespaces = ()
 
@@ -109,10 +108,9 @@ class CustomModelConfiguration(BaseModel):
     credentials: dict
 
     if PYDANTIC_V2:
-        model_config = ConfigDict(
+        model_config = ConfigDict(protected_namespaces=())
-            protected_namespaces=()
-        )
     else:
+
         class Config:
             protected_namespaces = ()
 

model-providers/model_providers/core/entities/queue_entities.py

@@ -1,13 +1,13 @@
 from enum import Enum
 from typing import Any
 
-from ..._models import BaseModel
-
 from model_providers.core.model_runtime.entities.llm_entities import (
     LLMResult,
     LLMResultChunk,
 )
 
+from ..._models import BaseModel
+
 
 class QueueEvent(Enum):
     """

model-providers/model_providers/core/model_runtime/entities/llm_entities.py

@@ -2,8 +2,6 @@ from decimal import Decimal
 from enum import Enum
 from typing import List, Optional
 
-from ...._models import BaseModel
-
 from model_providers.core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
     PromptMessage,
@@ -13,6 +11,8 @@ from model_providers.core.model_runtime.entities.model_entities import (
     PriceInfo,
 )
 
+from ...._models import BaseModel
+
 
 class LLMMode(Enum):
     """

model-providers/model_providers/core/model_runtime/entities/model_entities.py

@@ -2,11 +2,11 @@ from decimal import Decimal
 from enum import Enum
 from typing import Any, Dict, List, Optional
 
+from model_providers.core.model_runtime.entities.common_entities import I18nObject
+
 from ...._compat import PYDANTIC_V2, ConfigDict
 from ...._models import BaseModel
 
-from model_providers.core.model_runtime.entities.common_entities import I18nObject
-
 
 class ModelType(Enum):
     """
@@ -164,10 +164,9 @@ class ProviderModel(BaseModel):
     model_properties: Dict[ModelPropertyKey, Any]
     deprecated: bool = False
     if PYDANTIC_V2:
-        model_config = ConfigDict(
+        model_config = ConfigDict(protected_namespaces=())
-            protected_namespaces=()
-        )
     else:
+
         class Config:
             protected_namespaces = ()
 

model-providers/model_providers/core/model_runtime/entities/provider_entities.py

@@ -1,9 +1,6 @@
 from enum import Enum
 from typing import List, Optional
 
-from ...._compat import PYDANTIC_V2, ConfigDict
-from ...._models import BaseModel
-
 from model_providers.core.model_runtime.entities.common_entities import I18nObject
 from model_providers.core.model_runtime.entities.model_entities import (
     AIModelEntity,
@@ -11,6 +8,9 @@ from model_providers.core.model_runtime.entities.model_entities import (
     ProviderModel,
 )
 
+from ...._compat import PYDANTIC_V2, ConfigDict
+from ...._models import BaseModel
+
 
 class ConfigurateMethod(Enum):
     """
@@ -136,10 +136,9 @@ class ProviderEntity(BaseModel):
     model_credential_schema: Optional[ModelCredentialSchema] = None
 
     if PYDANTIC_V2:
-        model_config = ConfigDict(
+        model_config = ConfigDict(protected_namespaces=())
-            protected_namespaces=()
-        )
     else:
+
         class Config:
             protected_namespaces = ()
 

model-providers/model_providers/core/model_runtime/entities/text_embedding_entities.py

@@ -1,10 +1,10 @@
 from decimal import Decimal
 from typing import List
 
-from ...._models import BaseModel
-
 from model_providers.core.model_runtime.entities.model_entities import ModelUsage
 
+from ...._models import BaseModel
+
 
 class EmbeddingUsage(ModelUsage):
     """

model-providers/model_providers/core/model_runtime/model_providers/azure_openai/_constant.py

@@ -1,5 +1,3 @@
-from ....._models import BaseModel
-
 from model_providers.core.model_runtime.entities.defaults import PARAMETER_RULE_TEMPLATE
 from model_providers.core.model_runtime.entities.llm_entities import LLMMode
 from model_providers.core.model_runtime.entities.model_entities import (
@@ -14,6 +12,8 @@ from model_providers.core.model_runtime.entities.model_entities import (
     PriceConfig,
 )
 
+from ....._models import BaseModel
+
 AZURE_OPENAI_API_VERSION = "2024-02-15-preview"
 
 

model-providers/model_providers/core/model_runtime/model_providers/cohere/llm/llm.py

@@ -1,6 +1,5 @@
 import logging
-from typing import Generator
+from typing import Dict, Generator, List, Optional, Type, Union, cast
-from typing import Dict, List, Optional, Type, Union, cast
 
 import cohere
 from cohere.responses import Chat, Generations

model-providers/model_providers/core/model_runtime/model_providers/deepseek/llm/llm.py

@@ -1,7 +1,6 @@
 import logging
-from typing import Generator
-from typing import List, Optional, Union, cast
 from decimal import Decimal
+from typing import Generator, List, Optional, Union, cast
 
 import tiktoken
 from openai import OpenAI, Stream
@@ -37,10 +36,15 @@ from model_providers.core.model_runtime.entities.message_entities import (
 )
 from model_providers.core.model_runtime.entities.model_entities import (
     AIModelEntity,
+    DefaultParameterName,
     FetchFrom,
     I18nObject,
+    ModelFeature,
+    ModelPropertyKey,
     ModelType,
-    PriceConfig, ParameterRule, ParameterType, ModelFeature, ModelPropertyKey, DefaultParameterName,
+    ParameterRule,
+    ParameterType,
+    PriceConfig,
 )
 from model_providers.core.model_runtime.errors.validate import (
     CredentialsValidateFailedError,
@@ -48,7 +52,9 @@ from model_providers.core.model_runtime.errors.validate import (
 from model_providers.core.model_runtime.model_providers.__base.large_language_model import (
     LargeLanguageModel,
 )
-from model_providers.core.model_runtime.model_providers.deepseek._common import _CommonDeepseek
+from model_providers.core.model_runtime.model_providers.deepseek._common import (
+    _CommonDeepseek,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -1117,7 +1123,7 @@ class DeepseekLargeLanguageModel(_CommonDeepseek, LargeLanguageModel):
         return num_tokens
 
     def get_customizable_model_schema(
-            self, model: str, credentials: dict
+        self, model: str, credentials: dict
     ) -> AIModelEntity:
         """
         Get customizable model schema.
@@ -1129,7 +1135,6 @@ class DeepseekLargeLanguageModel(_CommonDeepseek, LargeLanguageModel):
         """
         extras = {}
 
-
         entity = AIModelEntity(
             model=model,
             label=I18nObject(zh_Hans=model, en_US=model),
@@ -1149,8 +1154,8 @@ class DeepseekLargeLanguageModel(_CommonDeepseek, LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="The temperature of the model. "
-                              "Increasing the temperature will make the model answer "
+                        "Increasing the temperature will make the model answer "
-                              "more creatively. (Default: 0.8)"
+                        "more creatively. (Default: 0.8)"
                     ),
                     default=0.8,
                     min=0,
@@ -1163,8 +1168,8 @@ class DeepseekLargeLanguageModel(_CommonDeepseek, LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Works together with top-k. A higher value (e.g., 0.95) will lead to "
-                              "more diverse text, while a lower value (e.g., 0.5) will generate more "
+                        "more diverse text, while a lower value (e.g., 0.5) will generate more "
-                              "focused and conservative text. (Default: 0.9)"
+                        "focused and conservative text. (Default: 0.9)"
                     ),
                     default=0.9,
                     min=0,
@@ -1177,8 +1182,8 @@ class DeepseekLargeLanguageModel(_CommonDeepseek, LargeLanguageModel):
                     help=I18nObject(
                         en_US="A number between -2.0 and 2.0. If positive, ",
                         zh_Hans="介于 -2.0 和 2.0 之间的数字。如果该值为正，"
-                                "那么新 token 会根据其在已有文本中的出现频率受到相应的惩罚，"
+                        "那么新 token 会根据其在已有文本中的出现频率受到相应的惩罚，"
-                                "降低模型重复相同内容的可能性"
+                        "降低模型重复相同内容的可能性",
                     ),
                     default=0,
                     min=-2,
@@ -1190,7 +1195,7 @@ class DeepseekLargeLanguageModel(_CommonDeepseek, LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Sets how strongly to presence_penalty. ",
-                        zh_Hans="介于 -2.0 和 2.0 之间的数字。如果该值为正，那么新 token 会根据其是否已在已有文本中出现受到相应的惩罚，从而增加模型谈论新主题的可能性。"
+                        zh_Hans="介于 -2.0 和 2.0 之间的数字。如果该值为正，那么新 token 会根据其是否已在已有文本中出现受到相应的惩罚，从而增加模型谈论新主题的可能性。",
                     ),
                     default=1.1,
                     min=-2,
@@ -1204,7 +1209,7 @@ class DeepseekLargeLanguageModel(_CommonDeepseek, LargeLanguageModel):
                     help=I18nObject(
                         en_US="Maximum number of tokens to predict when generating text. ",
                         zh_Hans="限制一次请求中模型生成 completion 的最大 token 数。"
-                                "输入 token 和输出 token 的总长度受模型的上下文长度的限制。"
+                        "输入 token 和输出 token 的总长度受模型的上下文长度的限制。",
                     ),
                     default=128,
                     min=-2,
@@ -1216,7 +1221,7 @@ class DeepseekLargeLanguageModel(_CommonDeepseek, LargeLanguageModel):
                     type=ParameterType.BOOLEAN,
                     help=I18nObject(
                         en_US="Whether to return the log probabilities of the tokens. ",
-                        zh_Hans="是否返回所输出 token 的对数概率。如果为 true，则在 message 的 content 中返回每个输出 token 的对数概率。"
+                        zh_Hans="是否返回所输出 token 的对数概率。如果为 true，则在 message 的 content 中返回每个输出 token 的对数概率。",
                     ),
                 ),
                 ParameterRule(
@@ -1226,7 +1231,7 @@ class DeepseekLargeLanguageModel(_CommonDeepseek, LargeLanguageModel):
                     help=I18nObject(
                         en_US="the format to return a response in.",
                         zh_Hans="一个介于 0 到 20 之间的整数 N，指定每个输出位置返回输出概率 top N 的 token，"
-                                "且返回这些 token 的对数概率。指定此参数时，logprobs 必须为 true。"
+                        "且返回这些 token 的对数概率。指定此参数时，logprobs 必须为 true。",
                     ),
                     default=0,
                     min=0,

model-providers/model_providers/core/model_runtime/model_providers/model_provider_factory.py

@@ -3,8 +3,6 @@ import logging
 import os
 from typing import Dict, List, Optional, Union
 
-from ...._models import BaseModel
-
 from model_providers.core.model_runtime.entities.model_entities import ModelType
 from model_providers.core.model_runtime.entities.provider_entities import (
     ProviderConfig,
@@ -25,6 +23,8 @@ from model_providers.core.utils.position_helper import (
     sort_to_dict_by_position_map,
 )
 
+from ...._models import BaseModel
+
 logger = logging.getLogger(__name__)
 
 

model-providers/model_providers/core/model_runtime/model_providers/moonshot/llm/llm.py

@@ -1,5 +1,4 @@
-from typing import Generator
+from typing import Generator, List, Optional, Union
-from typing import List, Optional, Union
 
 from model_providers.core.model_runtime.entities.llm_entities import LLMResult
 from model_providers.core.model_runtime.entities.message_entities import (

model-providers/model_providers/core/model_runtime/model_providers/ollama/llm/llm.py

@@ -1,7 +1,6 @@
 import logging
-from typing import Generator
-from typing import List, Optional, Union, cast
 from decimal import Decimal
+from typing import Generator, List, Optional, Union, cast
 
 import tiktoken
 from openai import OpenAI, Stream
@@ -37,10 +36,15 @@ from model_providers.core.model_runtime.entities.message_entities import (
 )
 from model_providers.core.model_runtime.entities.model_entities import (
     AIModelEntity,
+    DefaultParameterName,
     FetchFrom,
     I18nObject,
+    ModelFeature,
+    ModelPropertyKey,
     ModelType,
-    PriceConfig, ModelFeature, ModelPropertyKey, DefaultParameterName, ParameterRule, ParameterType,
+    ParameterRule,
+    ParameterType,
+    PriceConfig,
 )
 from model_providers.core.model_runtime.errors.validate import (
     CredentialsValidateFailedError,
@@ -48,7 +52,9 @@ from model_providers.core.model_runtime.errors.validate import (
 from model_providers.core.model_runtime.model_providers.__base.large_language_model import (
     LargeLanguageModel,
 )
-from model_providers.core.model_runtime.model_providers.ollama._common import _CommonOllama
+from model_providers.core.model_runtime.model_providers.ollama._common import (
+    _CommonOllama,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -661,7 +667,7 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
             messages=[self._convert_prompt_message_to_dict(m) for m in prompt_messages],
             model=model,
             stream=stream,
-            extra_body=extra_body
+            extra_body=extra_body,
         )
 
         if stream:
@@ -1120,7 +1126,7 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
         return num_tokens
 
     def get_customizable_model_schema(
-            self, model: str, credentials: dict
+        self, model: str, credentials: dict
     ) -> AIModelEntity:
         """
         Get customizable model schema.
@@ -1154,8 +1160,8 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="The temperature of the model. "
-                              "Increasing the temperature will make the model answer "
+                        "Increasing the temperature will make the model answer "
-                              "more creatively. (Default: 0.8)"
+                        "more creatively. (Default: 0.8)"
                     ),
                     default=0.8,
                     min=0,
@@ -1168,8 +1174,8 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Works together with top-k. A higher value (e.g., 0.95) will lead to "
-                              "more diverse text, while a lower value (e.g., 0.5) will generate more "
+                        "more diverse text, while a lower value (e.g., 0.5) will generate more "
-                              "focused and conservative text. (Default: 0.9)"
+                        "focused and conservative text. (Default: 0.9)"
                     ),
                     default=0.9,
                     min=0,
@@ -1181,8 +1187,8 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Reduces the probability of generating nonsense. "
-                              "A higher value (e.g. 100) will give more diverse answers, "
+                        "A higher value (e.g. 100) will give more diverse answers, "
-                              "while a lower value (e.g. 10) will be more conservative. (Default: 40)"
+                        "while a lower value (e.g. 10) will be more conservative. (Default: 40)"
                     ),
                     default=40,
                     min=1,
@@ -1194,8 +1200,8 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Sets how strongly to penalize repetitions. "
-                              "A higher value (e.g., 1.5) will penalize repetitions more strongly, "
+                        "A higher value (e.g., 1.5) will penalize repetitions more strongly, "
-                              "while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)"
+                        "while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)"
                     ),
                     default=1.1,
                     min=-2,
@@ -1208,7 +1214,7 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Maximum number of tokens to predict when generating text. "
-                              "(Default: 128, -1 = infinite generation, -2 = fill context)"
+                        "(Default: 128, -1 = infinite generation, -2 = fill context)"
                     ),
                     default=128,
                     min=-2,
@@ -1220,7 +1226,7 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Enable Mirostat sampling for controlling perplexity. "
-                              "(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"
+                        "(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"
                     ),
                     default=0,
                     min=0,
@@ -1232,9 +1238,9 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Influences how quickly the algorithm responds to feedback from "
-                              "the generated text. A lower learning rate will result in slower adjustments, "
+                        "the generated text. A lower learning rate will result in slower adjustments, "
-                              "while a higher learning rate will make the algorithm more responsive. "
+                        "while a higher learning rate will make the algorithm more responsive. "
-                              "(Default: 0.1)"
+                        "(Default: 0.1)"
                     ),
                     default=0.1,
                     precision=1,
@@ -1245,7 +1251,7 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Controls the balance between coherence and diversity of the output. "
-                              "A lower value will result in more focused and coherent text. (Default: 5.0)"
+                        "A lower value will result in more focused and coherent text. (Default: 5.0)"
                     ),
                     default=5.0,
                     precision=1,
@@ -1256,7 +1262,7 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Sets the size of the context window used to generate the next token. "
-                              "(Default: 2048)"
+                        "(Default: 2048)"
                     ),
                     default=2048,
                     min=1,
@@ -1267,7 +1273,7 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="The number of layers to send to the GPU(s). "
-                              "On macOS it defaults to 1 to enable metal support, 0 to disable."
+                        "On macOS it defaults to 1 to enable metal support, 0 to disable."
                     ),
                     default=1,
                     min=0,
@@ -1279,9 +1285,9 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Sets the number of threads to use during computation. "
-                              "By default, Ollama will detect this for optimal performance. "
+                        "By default, Ollama will detect this for optimal performance. "
-                              "It is recommended to set this value to the number of physical CPU cores "
+                        "It is recommended to set this value to the number of physical CPU cores "
-                              "your system has (as opposed to the logical number of cores)."
+                        "your system has (as opposed to the logical number of cores)."
                     ),
                     min=1,
                 ),
@@ -1291,7 +1297,7 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Sets how far back for the model to look back to prevent repetition. "
-                              "(Default: 64, 0 = disabled, -1 = num_ctx)"
+                        "(Default: 64, 0 = disabled, -1 = num_ctx)"
                     ),
                     default=64,
                     min=-1,
@@ -1302,8 +1308,8 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(
                         en_US="Tail free sampling is used to reduce the impact of less probable tokens "
-                              "from the output. A higher value (e.g., 2.0) will reduce the impact more, "
+                        "from the output. A higher value (e.g., 2.0) will reduce the impact more, "
-                              "while a value of 1.0 disables this setting. (default: 1)"
+                        "while a value of 1.0 disables this setting. (default: 1)"
                     ),
                     default=1,
                     precision=1,
@@ -1314,8 +1320,8 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(
                         en_US="Sets the random number seed to use for generation. Setting this to "
-                              "a specific number will make the model generate the same text for "
+                        "a specific number will make the model generate the same text for "
-                              "the same prompt. (Default: 0)"
+                        "the same prompt. (Default: 0)"
                     ),
                     default=0,
                 ),
@@ -1325,7 +1331,7 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
                     type=ParameterType.STRING,
                     help=I18nObject(
                         en_US="the format to return a response in."
-                              " Currently the only accepted value is json."
+                        " Currently the only accepted value is json."
                     ),
                     options=["json"],
                 ),

model-providers/model_providers/core/model_runtime/model_providers/openai/llm/llm.py

@@ -1,6 +1,5 @@
 import logging
-from typing import Generator
+from typing import Generator, List, Optional, Union, cast
-from typing import List, Optional, Union, cast
 
 import tiktoken
 from openai import OpenAI, Stream

model-providers/model_providers/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py

@@ -1,8 +1,7 @@
 import json
 import logging
-from typing import Generator
 from decimal import Decimal
-from typing import List, Optional, Union, cast
+from typing import Generator, List, Optional, Union, cast
 from urllib.parse import urljoin
 
 import requests

model-providers/model_providers/core/model_runtime/model_providers/openllm/llm/openllm_generate.py

@@ -1,7 +1,6 @@
-from typing import Generator
 from enum import Enum
 from json import dumps, loads
-from typing import Any, Union
+from typing import Any, Generator, Union
 
 from requests import Response, post
 from requests.exceptions import ConnectionError, InvalidSchema, MissingSchema

model-providers/model_providers/core/model_runtime/model_providers/spark/llm/llm.py

@@ -1,6 +1,5 @@
 import threading
-from typing import Generator
+from typing import Dict, Generator, List, Optional, Type, Union
-from typing import Dict, List, Optional, Type, Union
 
 from model_providers.core.model_runtime.entities.llm_entities import (
     LLMResult,

model-providers/model_providers/core/model_runtime/model_providers/togetherai/llm/llm.py

@@ -1,5 +1,4 @@
-from typing import Generator
+from typing import Generator, List, Optional, Union
-from typing import List, Optional, Union
 
 from model_providers.core.model_runtime.entities.llm_entities import LLMResult
 from model_providers.core.model_runtime.entities.message_entities import (

model-providers/model_providers/core/model_runtime/model_providers/tongyi/llm/llm.py

@@ -1,5 +1,4 @@
-from typing import Generator
+from typing import Dict, Generator, List, Optional, Type, Union
-from typing import Dict, List, Optional, Type, Union
 
 from dashscope import get_tokenizer
 from dashscope.api_entities.dashscope_response import DashScopeAPIResponse

model-providers/model_providers/core/model_runtime/model_providers/xinference/llm/llm.py

@@ -1,6 +1,4 @@
-from typing import Generator, Iterator
+from typing import Dict, Generator, Iterator, List, Type, Union, cast
-
-from typing import Dict, List, Union, cast, Type
 
 from openai import (
     APIConnectionError,
@@ -527,8 +525,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
     def _extract_response_tool_calls(
         self,
         response_tool_calls: Union[
-            List[ChatCompletionMessageToolCall],
+            List[ChatCompletionMessageToolCall], List[ChoiceDeltaToolCall]
-            List[ChoiceDeltaToolCall]
         ],
     ) -> List[AssistantPromptMessage.ToolCall]:
         """

model-providers/model_providers/core/model_runtime/model_providers/zhipuai/llm/llm.py

@@ -195,8 +195,10 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         if stop:
             extra_model_kwargs["stop"] = stop
 
-        client = ZhipuAI(base_url=credentials_kwargs["api_base"],
+        client = ZhipuAI(
-                         api_key=credentials_kwargs["api_key"])
+            base_url=credentials_kwargs["api_base"],
+            api_key=credentials_kwargs["api_key"],
+        )
 
         if len(prompt_messages) == 0:
             raise ValueError("At least one message is required")

model-providers/model_providers/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py

@@ -43,8 +43,10 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
         :return: embeddings result
         """
         credentials_kwargs = self._to_credential_kwargs(credentials)
-        client = ZhipuAI(base_url=credentials_kwargs["api_base"],
+        client = ZhipuAI(
-                         api_key=credentials_kwargs["api_key"])
+            base_url=credentials_kwargs["api_base"],
+            api_key=credentials_kwargs["api_key"],
+        )
 
         embeddings, embedding_used_tokens = self.embed_documents(model, client, texts)
 
@@ -85,8 +87,10 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
         try:
             # transform credentials to kwargs for model instance
             credentials_kwargs = self._to_credential_kwargs(credentials)
-            client = ZhipuAI(base_url=credentials_kwargs["api_base"],
+            client = ZhipuAI(
-                             api_key=credentials_kwargs["api_key"])
+                base_url=credentials_kwargs["api_base"],
+                api_key=credentials_kwargs["api_key"],
+            )
 
             # call embedding model
             self.embed_documents(

model-providers/model_providers/core/model_runtime/utils/helper.py

@@ -1,4 +1,5 @@
 import pydantic
+
 from ...._models import BaseModel
 
 

model-providers/model_providers/core/utils/json_dumps.py

@@ -1,6 +1,7 @@
 import os
 
 import orjson
+
 from ..._models import BaseModel
 
 

model-providers/model_providers/extensions/ext_storage.py

@@ -1,8 +1,7 @@
 import os
 import shutil
-from typing import Generator
 from contextlib import closing
-from typing import Union
+from typing import Generator, Union
 
 import boto3
 from botocore.exceptions import ClientError

model-providers/tests/conftest.py

@@ -104,15 +104,17 @@ def logging_conf() -> dict:
         111,
     )
 
+
 @pytest.fixture
 def providers_file(request) -> str:
-    from pathlib import Path
     import os
+    from pathlib import Path
+
     # 当前执行目录
     # 获取当前测试文件的路径
     test_file_path = Path(str(request.fspath)).parent
-    print("test_file_path:",test_file_path)
+    print("test_file_path:", test_file_path)
-    return os.path.join(test_file_path,"model_providers.yaml")
+    return os.path.join(test_file_path, "model_providers.yaml")
 
 
 @pytest.fixture
@@ -121,9 +123,7 @@ def init_server(logging_conf: dict, providers_file: str) -> None:
     try:
         boot = (
             BootstrapWebBuilder()
-            .model_providers_cfg_path(
+            .model_providers_cfg_path(model_providers_cfg_path=providers_file)
-                model_providers_cfg_path=providers_file
-            )
             .host(host="127.0.0.1")
             .port(port=20000)
             .build()
@@ -139,5 +139,4 @@ def init_server(logging_conf: dict, providers_file: str) -> None:
             boot.destroy()
 
     except SystemExit:
-
         raise

model-providers/tests/integration_tests/deepseek_providers_test/test_deepseek_service.py

@@ -1,15 +1,20 @@
+import logging
+
+import pytest
 from langchain.chains import LLMChain
 from langchain_core.prompts import PromptTemplate
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-import pytest
-import logging
 
 logger = logging.getLogger(__name__)
 
 
 @pytest.mark.requires("openai")
 def test_llm(init_server: str):
-    llm = ChatOpenAI(model_name="deepseek-chat", openai_api_key="sk-dcb625fcbc1e497d80b7b9493b51d758", openai_api_base=f"{init_server}/deepseek/v1")
+    llm = ChatOpenAI(
+        model_name="deepseek-chat",
+        openai_api_key="sk-dcb625fcbc1e497d80b7b9493b51d758",
+        openai_api_base=f"{init_server}/deepseek/v1",
+    )
     template = """Question: {question}
     
     Answer: Let's think step by step."""

model-providers/tests/integration_tests/ollama_providers_test/test_ollama_service.py

@@ -1,15 +1,20 @@
+import logging
+
+import pytest
 from langchain.chains import LLMChain
 from langchain_core.prompts import PromptTemplate
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-import pytest
-import logging
 
 logger = logging.getLogger(__name__)
 
 
 @pytest.mark.requires("openai")
 def test_llm(init_server: str):
-    llm = ChatOpenAI(model_name="llama3", openai_api_key="YOUR_API_KEY", openai_api_base=f"{init_server}/ollama/v1")
+    llm = ChatOpenAI(
+        model_name="llama3",
+        openai_api_key="YOUR_API_KEY",
+        openai_api_base=f"{init_server}/ollama/v1",
+    )
     template = """Question: {question}
     
     Answer: Let's think step by step."""
@@ -23,9 +28,11 @@ def test_llm(init_server: str):
 
 @pytest.mark.requires("openai")
 def test_embedding(init_server: str):
-    embeddings = OpenAIEmbeddings(model="text-embedding-3-large",
+    embeddings = OpenAIEmbeddings(
-                                  openai_api_key="YOUR_API_KEY",
+        model="text-embedding-3-large",
-                                  openai_api_base=f"{init_server}/zhipuai/v1")
+        openai_api_key="YOUR_API_KEY",
+        openai_api_base=f"{init_server}/zhipuai/v1",
+    )
 
     text = "你好"
 

model-providers/tests/integration_tests/openai_providers_test/test_openai_service.py

@@ -1,15 +1,18 @@
+import logging
+
+import pytest
 from langchain.chains import LLMChain
 from langchain_core.prompts import PromptTemplate
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-import pytest
-import logging
 
 logger = logging.getLogger(__name__)
 
 
 @pytest.mark.requires("openai")
 def test_llm(init_server: str):
-    llm = ChatOpenAI(openai_api_key="YOUR_API_KEY", openai_api_base=f"{init_server}/openai/v1")
+    llm = ChatOpenAI(
+        openai_api_key="YOUR_API_KEY", openai_api_base=f"{init_server}/openai/v1"
+    )
     template = """Question: {question}
     
     Answer: Let's think step by step."""
@@ -21,17 +24,16 @@ def test_llm(init_server: str):
     logger.info("\033[1;32m" + f"llm_chain: {responses}" + "\033[0m")
 
 
-
-
 @pytest.mark.requires("openai")
 def test_embedding(init_server: str):
-
+    embeddings = OpenAIEmbeddings(
-    embeddings = OpenAIEmbeddings(model="text-embedding-3-large",
+        model="text-embedding-3-large",
-                                  openai_api_key="YOUR_API_KEY",
+        openai_api_key="YOUR_API_KEY",
-                                  openai_api_base=f"{init_server}/zhipuai/v1")
+        openai_api_base=f"{init_server}/zhipuai/v1",
+    )
 
     text = "你好"
 
     query_result = embeddings.embed_query(text)
 
-    logger.info("\033[1;32m" + f"embeddings: {query_result}" + "\033[0m")
+    logger.info("\033[1;32m" + f"embeddings: {query_result}" + "\033[0m")

model-providers/tests/integration_tests/xinference_providers_test/test_xinference_service.py

@@ -1,8 +1,9 @@
+import logging
+
+import pytest
 from langchain.chains import LLMChain
 from langchain_core.prompts import PromptTemplate
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-import pytest
-import logging
 
 logger = logging.getLogger(__name__)
 
@@ -10,9 +11,10 @@ logger = logging.getLogger(__name__)
 @pytest.mark.requires("xinference_client")
 def test_llm(init_server: str):
     llm = ChatOpenAI(
-
         model_name="glm-4",
-        openai_api_key="YOUR_API_KEY", openai_api_base=f"{init_server}/xinference/v1")
+        openai_api_key="YOUR_API_KEY",
+        openai_api_base=f"{init_server}/xinference/v1",
+    )
     template = """Question: {question}
     
     Answer: Let's think step by step."""
@@ -26,9 +28,11 @@ def test_llm(init_server: str):
 
 @pytest.mark.requires("xinference-client")
 def test_embedding(init_server: str):
-    embeddings = OpenAIEmbeddings(model="text_embedding",
+    embeddings = OpenAIEmbeddings(
-                                  openai_api_key="YOUR_API_KEY",
+        model="text_embedding",
-                                  openai_api_base=f"{init_server}/xinference/v1")
+        openai_api_key="YOUR_API_KEY",
+        openai_api_base=f"{init_server}/xinference/v1",
+    )
 
     text = "你好"
 

model-providers/tests/integration_tests/zhipuai_providers_test/test_zhipuai_service.py

@@ -1,17 +1,20 @@
+import logging
+
+import pytest
 from langchain.chains import LLMChain
 from langchain_core.prompts import PromptTemplate
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-import pytest
-import logging
 
 logger = logging.getLogger(__name__)
 
+
 @pytest.mark.requires("zhipuai")
 def test_llm(init_server: str):
     llm = ChatOpenAI(
-
         model_name="glm-4",
-        openai_api_key="YOUR_API_KEY", openai_api_base=f"{init_server}/zhipuai/v1")
+        openai_api_key="YOUR_API_KEY",
+        openai_api_base=f"{init_server}/zhipuai/v1",
+    )
     template = """Question: {question}
     
     Answer: Let's think step by step."""
@@ -25,15 +28,14 @@ def test_llm(init_server: str):
 
 @pytest.mark.requires("zhipuai")
 def test_embedding(init_server: str):
-
+    embeddings = OpenAIEmbeddings(
-    embeddings = OpenAIEmbeddings(model="text_embedding",
+        model="text_embedding",
-                                  openai_api_key="YOUR_API_KEY",
+        openai_api_key="YOUR_API_KEY",
-                                  openai_api_base=f"{init_server}/zhipuai/v1")
+        openai_api_base=f"{init_server}/zhipuai/v1",
+    )
 
     text = "你好"
 
     query_result = embeddings.embed_query(text)
 
     logger.info("\033[1;32m" + f"embeddings: {query_result}" + "\033[0m")
-
-

model-providers/tests/unit_tests/deepseek/test_deepseek_provider_manager_models.py

@@ -7,7 +7,10 @@ from omegaconf import OmegaConf
 
 from model_providers import BootstrapWebBuilder, _to_custom_provide_configuration
 from model_providers.core.model_manager import ModelManager
-from model_providers.core.model_runtime.entities.model_entities import ModelType, AIModelEntity
+from model_providers.core.model_runtime.entities.model_entities import (
+    AIModelEntity,
+    ModelType,
+)
 from model_providers.core.provider_manager import ProviderManager
 
 logger = logging.getLogger(__name__)
@@ -16,9 +19,7 @@ logger = logging.getLogger(__name__)
 def test_provider_manager_models(logging_conf: dict, providers_file: str) -> None:
     logging.config.dictConfig(logging_conf)  # type: ignore
     # 读取配置文件
-    cfg = OmegaConf.load(
+    cfg = OmegaConf.load(providers_file)
-        providers_file
-    )
     # 转换配置文件
     (
         provider_name_to_provider_records_dict,
@@ -35,15 +36,14 @@ def test_provider_manager_models(logging_conf: dict, providers_file: str) -> Non
     )
     llm_models: List[AIModelEntity] = []
     for model in provider_model_bundle_llm.configuration.custom_configuration.models:
-
+        llm_models.append(
-        llm_models.append(provider_model_bundle_llm.model_type_instance.get_model_schema(
+            provider_model_bundle_llm.model_type_instance.get_model_schema(
-            model=model.model,
+                model=model.model,
-            credentials=model.credentials,
+                credentials=model.credentials,
-        ))
+            )
+        )
 
     # 获取预定义模型
-    llm_models.extend(
+    llm_models.extend(provider_model_bundle_llm.model_type_instance.predefined_models())
-        provider_model_bundle_llm.model_type_instance.predefined_models()
-    )
 
     logger.info(f"predefined_models: {llm_models}")

model-providers/tests/unit_tests/ollama/test_ollama_provider_manager_models.py

@@ -15,9 +15,7 @@ logger = logging.getLogger(__name__)
 def test_provider_manager_models(logging_conf: dict, providers_file: str) -> None:
     logging.config.dictConfig(logging_conf)  # type: ignore
     # 读取配置文件
-    cfg = OmegaConf.load(
+    cfg = OmegaConf.load(providers_file)
-        providers_file
-    )
     # 转换配置文件
     (
         provider_name_to_provider_records_dict,
@@ -34,16 +32,14 @@ def test_provider_manager_models(logging_conf: dict, providers_file: str) -> Non
     )
     llm_models: List[AIModelEntity] = []
     for model in provider_model_bundle_llm.configuration.custom_configuration.models:
-
+        llm_models.append(
-        llm_models.append(provider_model_bundle_llm.model_type_instance.get_model_schema(
+            provider_model_bundle_llm.model_type_instance.get_model_schema(
-            model=model.model,
+                model=model.model,
-            credentials=model.credentials,
+                credentials=model.credentials,
-        ))
+            )
+        )
 
     # 获取预定义模型
-    llm_models.extend(
+    llm_models.extend(provider_model_bundle_llm.model_type_instance.predefined_models())
-        provider_model_bundle_llm.model_type_instance.predefined_models()
-    )
 
     logger.info(f"predefined_models: {llm_models}")
-

model-providers/tests/unit_tests/openai/test_openai_provider_manager_models.py

@@ -15,9 +15,7 @@ logger = logging.getLogger(__name__)
 def test_provider_manager_models(logging_conf: dict, providers_file: str) -> None:
     logging.config.dictConfig(logging_conf)  # type: ignore
     # 读取配置文件
-    cfg = OmegaConf.load(
+    cfg = OmegaConf.load(providers_file)
-        providers_file
-    )
     # 转换配置文件
     (
         provider_name_to_provider_records_dict,
@@ -34,15 +32,14 @@ def test_provider_manager_models(logging_conf: dict, providers_file: str) -> Non
     )
     llm_models: List[AIModelEntity] = []
     for model in provider_model_bundle_llm.configuration.custom_configuration.models:
-
+        llm_models.append(
-        llm_models.append(provider_model_bundle_llm.model_type_instance.get_model_schema(
+            provider_model_bundle_llm.model_type_instance.get_model_schema(
-            model=model.model,
+                model=model.model,
-            credentials=model.credentials,
+                credentials=model.credentials,
-        ))
+            )
+        )
 
     # 获取预定义模型
-    llm_models.extend(
+    llm_models.extend(provider_model_bundle_llm.model_type_instance.predefined_models())
-        provider_model_bundle_llm.model_type_instance.predefined_models()
-    )
 
     logger.info(f"predefined_models: {llm_models}")

model-providers/tests/unit_tests/xinference/test_xinference_provider_manager_models.py

@@ -15,9 +15,7 @@ logger = logging.getLogger(__name__)
 def test_provider_manager_models(logging_conf: dict, providers_file: str) -> None:
     logging.config.dictConfig(logging_conf)  # type: ignore
     # 读取配置文件
-    cfg = OmegaConf.load(
+    cfg = OmegaConf.load(providers_file)
-        providers_file
-    )
     # 转换配置文件
     (
         provider_name_to_provider_records_dict,
@@ -34,15 +32,14 @@ def test_provider_manager_models(logging_conf: dict, providers_file: str) -> Non
     )
     llm_models: List[AIModelEntity] = []
     for model in provider_model_bundle_llm.configuration.custom_configuration.models:
-
+        llm_models.append(
-        llm_models.append(provider_model_bundle_llm.model_type_instance.get_model_schema(
+            provider_model_bundle_llm.model_type_instance.get_model_schema(
-            model=model.model,
+                model=model.model,
-            credentials=model.credentials,
+                credentials=model.credentials,
-        ))
+            )
+        )
 
     # 获取预定义模型
-    llm_models.extend(
+    llm_models.extend(provider_model_bundle_llm.model_type_instance.predefined_models())
-        provider_model_bundle_llm.model_type_instance.predefined_models()
-    )
 
     logger.info(f"predefined_models: {llm_models}")

model-providers/tests/unit_tests/zhipuai/test_zhipuai_provider_manager_models.py

@@ -15,9 +15,7 @@ logger = logging.getLogger(__name__)
 def test_provider_manager_models(logging_conf: dict, providers_file: str) -> None:
     logging.config.dictConfig(logging_conf)  # type: ignore
     # 读取配置文件
-    cfg = OmegaConf.load(
+    cfg = OmegaConf.load(providers_file)
-        providers_file
-    )
     # 转换配置文件
     (
         provider_name_to_provider_records_dict,
@@ -34,15 +32,14 @@ def test_provider_manager_models(logging_conf: dict, providers_file: str) -> Non
     )
     llm_models: List[AIModelEntity] = []
     for model in provider_model_bundle_llm.configuration.custom_configuration.models:
-
+        llm_models.append(
-        llm_models.append(provider_model_bundle_llm.model_type_instance.get_model_schema(
+            provider_model_bundle_llm.model_type_instance.get_model_schema(
-            model=model.model,
+                model=model.model,
-            credentials=model.credentials,
+                credentials=model.credentials,
-        ))
+            )
+        )
 
     # 获取预定义模型
-    llm_models.extend(
+    llm_models.extend(provider_model_bundle_llm.model_type_instance.predefined_models())
-        provider_model_bundle_llm.model_type_instance.predefined_models()
-    )
 
     logger.info(f"predefined_models: {llm_models}")