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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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79
libs/partners/openai/langchain_openai/llms/base.py
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@@ -2,21 +2,8 @@ from __future__ import annotations
import logging
import sys
from typing import (
AbstractSet,
Any,
AsyncIterator,
Collection,
Dict,
Iterator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,
Union,
)
from collections.abc import AsyncIterator, Collection, Iterator, Mapping
from typing import Any, Literal, Optional, Union
import openai
import tiktoken
@@ -35,7 +22,7 @@ logger = logging.getLogger(__name__)
def _update_token_usage(
keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
keys: set[str], response: dict[str, Any], token_usage: dict[str, Any]
) -> None:
"""Update token usage."""
_keys_to_use = keys.intersection(response["usage"])
@@ -47,7 +34,7 @@ def _update_token_usage(
def _stream_response_to_generation_chunk(
stream_response: Dict[str, Any],
stream_response: dict[str, Any],
) -> GenerationChunk:
"""Convert a stream response to a generation chunk."""
if not stream_response["choices"]:
@@ -84,7 +71,7 @@ class BaseOpenAI(BaseLLM):
"""How many completions to generate for each prompt."""
best_of: int = 1
"""Generates best_of completions server-side and returns the "best"."""
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)
@@ -108,12 +95,12 @@ class BaseOpenAI(BaseLLM):
)
batch_size: int = 20
"""Batch size to use when passing multiple documents to generate."""
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
None."""
logit_bias: Optional[Dict[str, float]] = None
logit_bias: Optional[dict[str, float]] = None
"""Adjust the probability of specific tokens being generated."""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
@@ -124,7 +111,7 @@ class BaseOpenAI(BaseLLM):
as well the chosen tokens."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
allowed_special: Union[Literal["all"], set[str]] = set()
"""Set of special tokens that are allowed。"""
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
"""Set of special tokens that are not allowed。"""
@@ -157,7 +144,7 @@ class BaseOpenAI(BaseLLM):
@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)
@@ -197,9 +184,9 @@ class BaseOpenAI(BaseLLM):
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."""
normal_params: Dict[str, Any] = {
normal_params: dict[str, Any] = {
"temperature": self.temperature,
"top_p": self.top_p,
"frequency_penalty": self.frequency_penalty,
@@ -228,7 +215,7 @@ class BaseOpenAI(BaseLLM):
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
@@ -255,7 +242,7 @@ class BaseOpenAI(BaseLLM):
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
@@ -283,8 +270,8 @@ class BaseOpenAI(BaseLLM):
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
prompts: list[str],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
@@ -307,7 +294,7 @@ class BaseOpenAI(BaseLLM):
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
token_usage: dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
@@ -363,8 +350,8 @@ class BaseOpenAI(BaseLLM):
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
prompts: list[str],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
@@ -373,7 +360,7 @@ class BaseOpenAI(BaseLLM):
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
token_usage: dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
@@ -419,10 +406,10 @@ class BaseOpenAI(BaseLLM):
def get_sub_prompts(
self,
params: Dict[str, Any],
prompts: List[str],
stop: Optional[List[str]] = None,
) -> List[List[str]]:
params: dict[str, Any],
prompts: list[str],
stop: Optional[list[str]] = None,
) -> list[list[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
params["stop"] = stop
@@ -441,9 +428,9 @@ class BaseOpenAI(BaseLLM):
def create_llm_result(
self,
choices: Any,
prompts: List[str],
params: Dict[str, Any],
token_usage: Dict[str, int],
prompts: list[str],
params: dict[str, Any],
token_usage: dict[str, int],
*,
system_fingerprint: Optional[str] = None,
) -> LLMResult:
@@ -470,7 +457,7 @@ class BaseOpenAI(BaseLLM):
return LLMResult(generations=generations, llm_output=llm_output)
@property
def _invocation_params(self) -> Dict[str, Any]:
def _invocation_params(self) -> dict[str, Any]:
"""Get the parameters used to invoke the model."""
return self._default_params
@@ -484,7 +471,7 @@ class BaseOpenAI(BaseLLM):
"""Return type of llm."""
return "openai"
def get_token_ids(self, text: str) -> List[int]:
def get_token_ids(self, text: str) -> list[int]:
"""Get the token IDs using the tiktoken package."""
if self.custom_get_token_ids is not None:
return self.custom_get_token_ids(text)
@@ -689,7 +676,7 @@ class OpenAI(BaseOpenAI):
""" # noqa: E501
@classmethod
def get_lc_namespace(cls) -> List[str]:
def get_lc_namespace(cls) -> list[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "llms", "openai"]
@@ -699,16 +686,16 @@ class OpenAI(BaseOpenAI):
return True
@property
def _invocation_params(self) -> Dict[str, Any]:
def _invocation_params(self) -> dict[str, Any]:
return {**{"model": self.model_name}, **super()._invocation_params}
@property
def lc_secrets(self) -> Dict[str, str]:
def lc_secrets(self) -> dict[str, str]:
return {"openai_api_key": "OPENAI_API_KEY"}
@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_api_base:
attributes["openai_api_base"] = self.openai_api_base