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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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15
libs/partners/openai/langchain_openai/llms/azure.py
View File

@@ -1,13 +1,14 @@
from __future__ import annotations
import logging
from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional, Union
from collections.abc import Awaitable, Mapping
from typing import Any, Callable, Optional, Union, cast
import openai
from langchain_core.language_models import LangSmithParams
from langchain_core.utils import from_env, secret_from_env
from pydantic import Field, SecretStr, model_validator
from typing_extensions import Self, cast
from typing_extensions import Self
from langchain_openai.llms.base import BaseOpenAI
@@ -91,12 +92,12 @@ class AzureOpenAI(BaseOpenAI):
"""
@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"]
@property
def lc_secrets(self) -> Dict[str, str]:
def lc_secrets(self) -> dict[str, str]:
return {
"openai_api_key": "AZURE_OPENAI_API_KEY",
"azure_ad_token": "AZURE_OPENAI_AD_TOKEN",
@@ -188,12 +189,12 @@ class AzureOpenAI(BaseOpenAI):
}
@property
def _invocation_params(self) -> Dict[str, Any]:
def _invocation_params(self) -> dict[str, Any]:
openai_params = {"model": self.deployment_name}
return {**openai_params, **super()._invocation_params}
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 = super()._get_ls_params(stop=stop, **kwargs)
@@ -209,7 +210,7 @@ class AzureOpenAI(BaseOpenAI):
return "azure"
@property
def lc_attributes(self) -> Dict[str, Any]:
def lc_attributes(self) -> dict[str, Any]:
return {
"openai_api_type": self.openai_api_type,
"openai_api_version": self.openai_api_version,

79
libs/partners/openai/langchain_openai/llms/base.py
View File

@@ -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