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partners[lint]: run pyupgrade to get code in line with 3.9 standards (#30781)

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Using `pyupgrade` to get all `partners` code up to 3.9 standards
(mostly, fixing old `typing` imports).
This commit is contained in:
Sydney Runkle
2025-04-11 07:18:44 -04:00
committed by GitHub
parent e72f3c26a0
commit 8c6734325b
123 changed files with 1000 additions and 1109 deletions
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129
libs/partners/openai/langchain_openai/chat_models/base.py
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@@ -10,6 +10,7 @@ import re
import ssl
import sys
import warnings
from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
from functools import partial
from io import BytesIO
from json import JSONDecodeError
@@ -18,17 +19,9 @@ from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Literal,
Mapping,
Optional,
Sequence,
Tuple,
Type,
TypedDict,
TypeVar,
Union,
@@ -137,7 +130,7 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
# Fix for azure
# Also OpenAI returns None for tool invocations
content = _dict.get("content", "") or ""
additional_kwargs: Dict = {}
additional_kwargs: dict = {}
if function_call := _dict.get("function_call"):
additional_kwargs["function_call"] = dict(function_call)
tool_calls = []
@@ -243,7 +236,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
Returns:
The dictionary.
"""
message_dict: Dict[str, Any] = {"content": _format_message_content(message.content)}
message_dict: dict[str, Any] = {"content": _format_message_content(message.content)}
if (name := message.name or message.additional_kwargs.get("name")) is not None:
message_dict["name"] = name
@@ -304,12 +297,12 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
def _convert_delta_to_message_chunk(
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
_dict: Mapping[str, Any], default_class: type[BaseMessageChunk]
) -> BaseMessageChunk:
id_ = _dict.get("id")
role = cast(str, _dict.get("role"))
content = cast(str, _dict.get("content") or "")
additional_kwargs: Dict = {}
additional_kwargs: dict = {}
if _dict.get("function_call"):
function_call = dict(_dict["function_call"])
if "name" in function_call and function_call["name"] is None:
@@ -418,8 +411,8 @@ class _FunctionCall(TypedDict):
_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM], Type]
_DictOrPydantic = Union[Dict, _BM]
_DictOrPydanticClass = Union[dict[str, Any], type[_BM], type]
_DictOrPydantic = Union[dict, _BM]
class _AllReturnType(TypedDict):
@@ -437,7 +430,7 @@ class BaseChatOpenAI(BaseChatModel):
"""Model name to use."""
temperature: Optional[float] = None
"""What sampling temperature to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[SecretStr] = Field(
alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
@@ -451,7 +444,7 @@ class BaseChatOpenAI(BaseChatModel):
openai_proxy: Optional[str] = Field(
default_factory=from_env("OPENAI_PROXY", default=None)
)
request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
request_timeout: Union[float, tuple[float, float], Any, None] = Field(
default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
@@ -476,7 +469,7 @@ class BaseChatOpenAI(BaseChatModel):
"""Number of most likely tokens to return at each token position, each with
an associated log probability. `logprobs` must be set to true
if this parameter is used."""
logit_bias: Optional[Dict[int, int]] = None
logit_bias: Optional[dict[int, int]] = None
"""Modify the likelihood of specified tokens appearing in the completion."""
streaming: bool = False
"""Whether to stream the results or not."""
@@ -517,14 +510,14 @@ class BaseChatOpenAI(BaseChatModel):
http_async_client: Union[Any, None] = Field(default=None, exclude=True)
"""Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you'd like a custom client for sync invocations."""
stop: Optional[Union[List[str], str]] = Field(default=None, alias="stop_sequences")
stop: Optional[Union[list[str], str]] = Field(default=None, alias="stop_sequences")
"""Default stop sequences."""
extra_body: Optional[Mapping[str, Any]] = None
"""Optional additional JSON properties to include in the request parameters when
making requests to OpenAI compatible APIs, such as vLLM."""
include_response_headers: bool = False
"""Whether to include response headers in the output message response_metadata."""
disabled_params: Optional[Dict[str, Any]] = Field(default=None)
disabled_params: Optional[dict[str, Any]] = Field(default=None)
"""Parameters of the OpenAI client or chat.completions endpoint that should be
disabled for the given model.
@@ -554,7 +547,7 @@ class BaseChatOpenAI(BaseChatModel):
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
def build_extra(cls, values: dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
values = _build_model_kwargs(values, all_required_field_names)
@@ -562,7 +555,7 @@ class BaseChatOpenAI(BaseChatModel):
@model_validator(mode="before")
@classmethod
def validate_temperature(cls, values: Dict[str, Any]) -> Any:
def validate_temperature(cls, values: dict[str, Any]) -> Any:
"""Currently o1 models only allow temperature=1."""
model = values.get("model_name") or values.get("model") or ""
if model.startswith("o1") and "temperature" not in values:
@@ -642,7 +635,7 @@ class BaseChatOpenAI(BaseChatModel):
return self
@property
def _default_params(self) -> Dict[str, Any]:
def _default_params(self) -> dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
exclude_if_none = {
"presence_penalty": self.presence_penalty,
@@ -669,7 +662,7 @@ class BaseChatOpenAI(BaseChatModel):
return params
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
def _combine_llm_outputs(self, llm_outputs: list[Optional[dict]]) -> dict:
overall_token_usage: dict = {}
system_fingerprint = None
for output in llm_outputs:
@@ -697,8 +690,8 @@ class BaseChatOpenAI(BaseChatModel):
def _convert_chunk_to_generation_chunk(
self,
chunk: dict,
default_chunk_class: Type,
base_generation_info: Optional[Dict],
default_chunk_class: type,
base_generation_info: Optional[dict],
) -> Optional[ChatGenerationChunk]:
if chunk.get("type") == "content.delta": # from beta.chat.completions.stream
return None
@@ -749,8 +742,8 @@ class BaseChatOpenAI(BaseChatModel):
def _stream_responses(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
@@ -783,8 +776,8 @@ class BaseChatOpenAI(BaseChatModel):
async def _astream_responses(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
@@ -838,8 +831,8 @@ class BaseChatOpenAI(BaseChatModel):
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
*,
stream_usage: Optional[bool] = None,
@@ -850,7 +843,7 @@ class BaseChatOpenAI(BaseChatModel):
if stream_usage:
kwargs["stream_options"] = {"include_usage": stream_usage}
payload = self._get_request_payload(messages, stop=stop, **kwargs)
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
default_chunk_class: type[BaseMessageChunk] = AIMessageChunk
base_generation_info = {}
if "response_format" in payload:
@@ -908,8 +901,8 @@ class BaseChatOpenAI(BaseChatModel):
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
@@ -965,7 +958,7 @@ class BaseChatOpenAI(BaseChatModel):
self,
input_: LanguageModelInput,
*,
stop: Optional[List[str]] = None,
stop: Optional[list[str]] = None,
**kwargs: Any,
) -> dict:
messages = self._convert_input(input_).to_messages()
@@ -982,7 +975,7 @@ class BaseChatOpenAI(BaseChatModel):
def _create_chat_result(
self,
response: Union[dict, openai.BaseModel],
generation_info: Optional[Dict] = None,
generation_info: Optional[dict] = None,
) -> ChatResult:
generations = []
@@ -1032,8 +1025,8 @@ class BaseChatOpenAI(BaseChatModel):
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
*,
stream_usage: Optional[bool] = None,
@@ -1044,7 +1037,7 @@ class BaseChatOpenAI(BaseChatModel):
if stream_usage:
kwargs["stream_options"] = {"include_usage": stream_usage}
payload = self._get_request_payload(messages, stop=stop, **kwargs)
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
default_chunk_class: type[BaseMessageChunk] = AIMessageChunk
base_generation_info = {}
if "response_format" in payload:
@@ -1106,8 +1099,8 @@ class BaseChatOpenAI(BaseChatModel):
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
@@ -1160,13 +1153,13 @@ class BaseChatOpenAI(BaseChatModel):
)
@property
def _identifying_params(self) -> Dict[str, Any]:
def _identifying_params(self) -> dict[str, Any]:
"""Get the identifying parameters."""
return {"model_name": self.model_name, **self._default_params}
def _get_invocation_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> Dict[str, Any]:
self, stop: Optional[list[str]] = None, **kwargs: Any
) -> dict[str, Any]:
"""Get the parameters used to invoke the model."""
return {
"model": self.model_name,
@@ -1176,7 +1169,7 @@ class BaseChatOpenAI(BaseChatModel):
}
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
self, stop: Optional[list[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
@@ -1199,7 +1192,7 @@ class BaseChatOpenAI(BaseChatModel):
"""Return type of chat model."""
return "openai-chat"
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
def _get_encoding_model(self) -> tuple[str, tiktoken.Encoding]:
if self.tiktoken_model_name is not None:
model = self.tiktoken_model_name
else:
@@ -1211,7 +1204,7 @@ class BaseChatOpenAI(BaseChatModel):
encoding = tiktoken.get_encoding(model)
return model, encoding
def get_token_ids(self, text: str) -> List[int]:
def get_token_ids(self, text: str) -> list[int]:
"""Get the tokens present in the text with tiktoken package."""
if self.custom_get_token_ids is not None:
return self.custom_get_token_ids(text)
@@ -1223,9 +1216,9 @@ class BaseChatOpenAI(BaseChatModel):
def get_num_tokens_from_messages(
self,
messages: List[BaseMessage],
messages: list[BaseMessage],
tools: Optional[
Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]
Sequence[Union[dict[str, Any], type, Callable, BaseTool]]
] = None,
) -> int:
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
@@ -1327,7 +1320,7 @@ class BaseChatOpenAI(BaseChatModel):
)
def bind_functions(
self,
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
functions: Sequence[Union[dict[str, Any], type[BaseModel], Callable, BaseTool]],
function_call: Optional[
Union[_FunctionCall, str, Literal["auto", "none"]]
] = None,
@@ -1380,7 +1373,7 @@ class BaseChatOpenAI(BaseChatModel):
def bind_tools(
self,
tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "none", "required", "any"], bool]
@@ -1727,7 +1720,7 @@ class BaseChatOpenAI(BaseChatModel):
else:
return llm | output_parser
def _filter_disabled_params(self, **kwargs: Any) -> Dict[str, Any]:
def _filter_disabled_params(self, **kwargs: Any) -> dict[str, Any]:
if not self.disabled_params:
return kwargs
filtered = {}
@@ -2301,17 +2294,17 @@ class ChatOpenAI(BaseChatOpenAI): # type: ignore[override]
"""Maximum number of tokens to generate."""
@property
def lc_secrets(self) -> Dict[str, str]:
def lc_secrets(self) -> dict[str, str]:
return {"openai_api_key": "OPENAI_API_KEY"}
@classmethod
def get_lc_namespace(cls) -> List[str]:
def get_lc_namespace(cls) -> list[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "chat_models", "openai"]
@property
def lc_attributes(self) -> Dict[str, Any]:
attributes: Dict[str, Any] = {}
def lc_attributes(self) -> dict[str, Any]:
attributes: dict[str, Any] = {}
if self.openai_organization:
attributes["openai_organization"] = self.openai_organization
@@ -2330,7 +2323,7 @@ class ChatOpenAI(BaseChatOpenAI): # type: ignore[override]
return True
@property
def _default_params(self) -> Dict[str, Any]:
def _default_params(self) -> dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
params = super()._default_params
if "max_tokens" in params:
@@ -2342,7 +2335,7 @@ class ChatOpenAI(BaseChatOpenAI): # type: ignore[override]
self,
input_: LanguageModelInput,
*,
stop: Optional[List[str]] = None,
stop: Optional[list[str]] = None,
**kwargs: Any,
) -> dict:
payload = super()._get_request_payload(input_, stop=stop, **kwargs)
@@ -2719,7 +2712,7 @@ def _lc_invalid_tool_call_to_openai_tool_call(
}
def _url_to_size(image_source: str) -> Optional[Tuple[int, int]]:
def _url_to_size(image_source: str) -> Optional[tuple[int, int]]:
try:
from PIL import Image # type: ignore[import]
except ImportError:
@@ -2771,7 +2764,7 @@ def _is_b64(s: str) -> bool:
return s.startswith("data:image")
def _resize(width: int, height: int) -> Tuple[int, int]:
def _resize(width: int, height: int) -> tuple[int, int]:
# larger side must be <= 2048
if width > 2048 or height > 2048:
if width > height:
@@ -2792,8 +2785,8 @@ def _resize(width: int, height: int) -> Tuple[int, int]:
def _convert_to_openai_response_format(
schema: Union[Dict[str, Any], Type], *, strict: Optional[bool] = None
) -> Union[Dict, TypeBaseModel]:
schema: Union[dict[str, Any], type], *, strict: Optional[bool] = None
) -> Union[dict, TypeBaseModel]:
if isinstance(schema, type) and is_basemodel_subclass(schema):
return schema
@@ -2815,8 +2808,10 @@ def _convert_to_openai_response_format(
function["schema"] = function.pop("parameters")
response_format = {"type": "json_schema", "json_schema": function}
if strict is not None and strict is not response_format["json_schema"].get(
"strict"
if (
strict is not None
and strict is not response_format["json_schema"].get("strict")
and isinstance(schema, dict)
):
msg = (
f"Output schema already has 'strict' value set to "
@@ -2829,7 +2824,7 @@ def _convert_to_openai_response_format(
def _oai_structured_outputs_parser(
ai_msg: AIMessage, schema: Type[_BM]
ai_msg: AIMessage, schema: type[_BM]
) -> Optional[PydanticBaseModel]:
if parsed := ai_msg.additional_kwargs.get("parsed"):
if isinstance(parsed, dict):
@@ -3141,7 +3136,7 @@ def _construct_responses_api_input(messages: Sequence[BaseMessage]) -> list:
def _construct_lc_result_from_responses_api(
response: Response,
schema: Optional[Type[_BM]] = None,
schema: Optional[type[_BM]] = None,
metadata: Optional[dict] = None,
) -> ChatResult:
"""Construct ChatResponse from OpenAI Response API response."""
@@ -3278,7 +3273,7 @@ def _construct_lc_result_from_responses_api(
def _convert_responses_chunk_to_generation_chunk(
chunk: Any, schema: Optional[Type[_BM]] = None, metadata: Optional[dict] = None
chunk: Any, schema: Optional[type[_BM]] = None, metadata: Optional[dict] = None
) -> Optional[ChatGenerationChunk]:
content = []
tool_call_chunks: list = []