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chore: drop UP045 (#33362)

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Python 3.9 EOL
This commit is contained in:
Mason Daugherty
2025-10-08 21:17:53 -04:00
committed by GitHub
parent 0039b3b046
commit 31eeb50ce0
119 changed files with 1423 additions and 1504 deletions
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74
libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py
View File

@@ -7,7 +7,7 @@ import json
from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
from dataclasses import dataclass
from operator import itemgetter
from typing import Any, Literal, Optional, Union, cast
from typing import Any, Literal, cast
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
@@ -316,7 +316,7 @@ class ChatHuggingFace(BaseChatModel):
the HuggingFace Hub.
Setup:
Install ``langchain-huggingface`` and ensure your Hugging Face token
Install `langchain-huggingface` and ensure your Hugging Face token
is saved.
.. code-block:: bash
@@ -478,33 +478,33 @@ class ChatHuggingFace(BaseChatModel):
HuggingFaceHub, or HuggingFacePipeline."""
tokenizer: Any = None
"""Tokenizer for the model. Only used for HuggingFacePipeline."""
model_id: Optional[str] = None
model_id: str | None = None
"""Model ID for the model. Only used for HuggingFaceEndpoint."""
temperature: Optional[float] = None
temperature: float | None = None
"""What sampling temperature to use."""
stop: Optional[Union[str, list[str]]] = Field(default=None, alias="stop_sequences")
stop: str | list[str] | None = Field(default=None, alias="stop_sequences")
"""Default stop sequences."""
presence_penalty: Optional[float] = None
presence_penalty: float | None = None
"""Penalizes repeated tokens."""
frequency_penalty: Optional[float] = None
frequency_penalty: float | None = None
"""Penalizes repeated tokens according to frequency."""
seed: Optional[int] = None
seed: int | None = None
"""Seed for generation"""
logprobs: Optional[bool] = None
logprobs: bool | None = None
"""Whether to return logprobs."""
top_logprobs: Optional[int] = None
top_logprobs: int | None = None
"""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: dict[int, int] | None = None
"""Modify the likelihood of specified tokens appearing in the completion."""
streaming: bool = False
"""Whether to stream the results or not."""
n: Optional[int] = None
n: int | None = None
"""Number of chat completions to generate for each prompt."""
top_p: Optional[float] = None
top_p: float | None = None
"""Total probability mass of tokens to consider at each step."""
max_tokens: Optional[int] = None
max_tokens: int | None = None
"""Maximum number of tokens to generate."""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
@@ -558,9 +558,9 @@ class ChatHuggingFace(BaseChatModel):
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, # noqa: FBT001
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
stream: bool | None = None, # noqa: FBT001
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
@@ -599,9 +599,9 @@ class ChatHuggingFace(BaseChatModel):
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None, # noqa: FBT001
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
stream: bool | None = None, # noqa: FBT001
**kwargs: Any,
) -> ChatResult:
if _is_huggingface_textgen_inference(self.llm):
@@ -638,8 +638,8 @@ class ChatHuggingFace(BaseChatModel):
def _stream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
if _is_huggingface_endpoint(self.llm):
@@ -687,8 +687,8 @@ class ChatHuggingFace(BaseChatModel):
async def _astream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
@@ -779,7 +779,7 @@ class ChatHuggingFace(BaseChatModel):
self.model_id = self.llm.repo_id
return
if _is_huggingface_textgen_inference(self.llm):
endpoint_url: Optional[str] = self.llm.inference_server_url
endpoint_url: str | None = self.llm.inference_server_url
if _is_huggingface_pipeline(self.llm):
from transformers import AutoTokenizer # type: ignore[import]
@@ -809,11 +809,9 @@ class ChatHuggingFace(BaseChatModel):
def bind_tools(
self,
tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
tools: Sequence[dict[str, Any] | type | Callable | BaseTool],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "none", "required"], bool] # noqa: PYI051
] = None,
tool_choice: dict | str | bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]:
"""Bind tool-like objects to this chat model.
@@ -826,7 +824,7 @@ class ChatHuggingFace(BaseChatModel):
`langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: Which tool to require the model to call.
Must be the name of the single provided function or
``'auto'`` to automatically determine which function to call
`'auto'` to automatically determine which function to call
(if any), or a dict of the form:
{"type": "function", "function": {"name": <<tool_name>>}}.
**kwargs: Any additional parameters to pass to the
@@ -870,14 +868,14 @@ class ChatHuggingFace(BaseChatModel):
def with_structured_output(
self,
schema: Optional[Union[dict, type[BaseModel]]] = None,
schema: dict | type[BaseModel] | None = None,
*,
method: Literal[
"function_calling", "json_mode", "json_schema"
] = "function_calling",
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
) -> Runnable[LanguageModelInput, dict | BaseModel]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
@@ -948,7 +946,7 @@ class ChatHuggingFace(BaseChatModel):
if is_pydantic_schema:
msg = "Pydantic schema is not supported for function calling"
raise NotImplementedError(msg)
output_parser: Union[JsonOutputKeyToolsParser, JsonOutputParser] = (
output_parser: JsonOutputKeyToolsParser | JsonOutputParser = (
JsonOutputKeyToolsParser(key_name=tool_name, first_tool_only=True)
)
elif method == "json_schema":
@@ -966,9 +964,7 @@ class ChatHuggingFace(BaseChatModel):
"schema": schema,
},
)
output_parser: Union[ # type: ignore[no-redef]
JsonOutputKeyToolsParser, JsonOutputParser
] = JsonOutputParser() # type: ignore[arg-type]
output_parser = JsonOutputParser() # type: ignore[arg-type]
elif method == "json_mode":
llm = self.bind(
response_format={"type": "json_object"},
@@ -977,9 +973,7 @@ class ChatHuggingFace(BaseChatModel):
"schema": schema,
},
)
output_parser: Union[ # type: ignore[no-redef]
JsonOutputKeyToolsParser, JsonOutputParser
] = JsonOutputParser() # type: ignore[arg-type]
output_parser = JsonOutputParser() # type: ignore[arg-type]
else:
msg = (
f"Unrecognized method argument. Expected one of 'function_calling' or "
@@ -999,7 +993,7 @@ class ChatHuggingFace(BaseChatModel):
return llm | output_parser
def _create_message_dicts(
self, messages: list[BaseMessage], stop: Optional[list[str]]
self, messages: list[BaseMessage], stop: list[str] | None
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
params = self._default_params
if stop is not None:

4
libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py
View File

@@ -1,6 +1,6 @@
from __future__ import annotations
from typing import Any, Optional
from typing import Any
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, Field
@@ -40,7 +40,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
model_name: str = Field(default=DEFAULT_MODEL_NAME, alias="model")
"""Model name to use."""
cache_folder: Optional[str] = None
cache_folder: str | None = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: dict[str, Any] = Field(default_factory=dict)

14
libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py
View File

@@ -1,7 +1,7 @@
from __future__ import annotations
import os
from typing import Any, Optional
from typing import Any
from langchain_core.embeddings import Embeddings
from langchain_core.utils import from_env
@@ -35,20 +35,20 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
client: Any = None #: :meta private:
async_client: Any = None #: :meta private:
model: Optional[str] = None
model: str | None = None
"""Model name to use."""
provider: Optional[str] = None
provider: str | None = None
"""Name of the provider to use for inference with the model specified in
``repo_id``. e.g. "sambanova". if not specified, defaults to HF Inference API.
available providers can be found in the [huggingface_hub documentation](https://huggingface.co/docs/huggingface_hub/guides/inference#supported-providers-and-tasks)."""
repo_id: Optional[str] = None
repo_id: str | None = None
"""Huggingfacehub repository id, for backward compatibility."""
task: Optional[str] = "feature-extraction"
task: str | None = "feature-extraction"
"""Task to call the model with."""
model_kwargs: Optional[dict] = None
model_kwargs: dict | None = None
"""Keyword arguments to pass to the model."""
huggingfacehub_api_token: Optional[str] = Field(
huggingfacehub_api_token: str | None = Field(
default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None)
)

52
libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py
View File

@@ -4,7 +4,7 @@ import inspect
import logging
import os
from collections.abc import AsyncIterator, Iterator, Mapping
from typing import Any, Optional
from typing import Any
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
@@ -79,42 +79,42 @@ class HuggingFaceEndpoint(LLM):
""" # noqa: E501
endpoint_url: Optional[str] = None
endpoint_url: str | None = None
"""Endpoint URL to use. If repo_id is not specified then this needs to given or
should be pass as env variable in `HF_INFERENCE_ENDPOINT`"""
repo_id: Optional[str] = None
repo_id: str | None = None
"""Repo to use. If endpoint_url is not specified then this needs to given"""
provider: Optional[str] = None
provider: str | None = None
"""Name of the provider to use for inference with the model specified in `repo_id`.
e.g. "cerebras". if not specified, Defaults to "auto" i.e. the first of the
providers available for the model, sorted by the user's order in https://hf.co/settings/inference-providers.
available providers can be found in the [huggingface_hub documentation](https://huggingface.co/docs/huggingface_hub/guides/inference#supported-providers-and-tasks)."""
huggingfacehub_api_token: Optional[str] = Field(
huggingfacehub_api_token: str | None = Field(
default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None)
)
max_new_tokens: int = 512
"""Maximum number of generated tokens"""
top_k: Optional[int] = None
top_k: int | None = None
"""The number of highest probability vocabulary tokens to keep for
top-k-filtering."""
top_p: Optional[float] = 0.95
top_p: float | None = 0.95
"""If set to < 1, only the smallest set of most probable tokens with probabilities
that add up to `top_p` or higher are kept for generation."""
typical_p: Optional[float] = 0.95
typical_p: float | None = 0.95
"""Typical Decoding mass. See [Typical Decoding for Natural Language
Generation](https://arxiv.org/abs/2202.00666) for more information."""
temperature: Optional[float] = 0.8
temperature: float | None = 0.8
"""The value used to module the logits distribution."""
repetition_penalty: Optional[float] = None
repetition_penalty: float | None = None
"""The parameter for repetition penalty. 1.0 means no penalty.
See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details."""
return_full_text: bool = False
"""Whether to prepend the prompt to the generated text"""
truncate: Optional[int] = None
truncate: int | None = None
"""Truncate inputs tokens to the given size"""
stop_sequences: list[str] = Field(default_factory=list)
"""Stop generating tokens if a member of `stop_sequences` is generated"""
seed: Optional[int] = None
seed: int | None = None
"""Random sampling seed"""
inference_server_url: str = ""
"""text-generation-inference instance base url"""
@@ -134,7 +134,7 @@ class HuggingFaceEndpoint(LLM):
model: str
client: Any = None #: :meta private:
async_client: Any = None #: :meta private:
task: Optional[str] = None
task: str | None = None
"""Task to call the model with. Should be a task that returns `generated_text`."""
model_config = ConfigDict(
@@ -292,7 +292,7 @@ class HuggingFaceEndpoint(LLM):
return "huggingface_endpoint"
def _invocation_params(
self, runtime_stop: Optional[list[str]], **kwargs: Any
self, runtime_stop: list[str] | None, **kwargs: Any
) -> dict[str, Any]:
params = {**self._default_params, **kwargs}
params["stop"] = params["stop"] + (runtime_stop or [])
@@ -301,8 +301,8 @@ class HuggingFaceEndpoint(LLM):
def _call(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
"""Call out to HuggingFace Hub's inference endpoint."""
@@ -331,8 +331,8 @@ class HuggingFaceEndpoint(LLM):
async def _acall(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
invocation_params = self._invocation_params(stop, **kwargs)
@@ -361,8 +361,8 @@ class HuggingFaceEndpoint(LLM):
def _stream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
invocation_params = self._invocation_params(stop, **kwargs)
@@ -371,13 +371,13 @@ class HuggingFaceEndpoint(LLM):
prompt, **invocation_params, stream=True
):
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
stop_seq_found: str | None = None
for stop_seq in invocation_params["stop"]:
if stop_seq in response:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
text: str | None = None
if stop_seq_found:
text = response[: response.index(stop_seq_found)]
else:
@@ -398,8 +398,8 @@ class HuggingFaceEndpoint(LLM):
async def _astream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
invocation_params = self._invocation_params(stop, **kwargs)
@@ -407,13 +407,13 @@ class HuggingFaceEndpoint(LLM):
prompt, **invocation_params, stream=True
):
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
stop_seq_found: str | None = None
for stop_seq in invocation_params["stop"]:
if stop_seq in response:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
text: str | None = None
if stop_seq_found:
text = response[: response.index(stop_seq_found)]
else:

24
libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
View File

@@ -3,7 +3,7 @@ from __future__ import annotations # type: ignore[import-not-found]
import importlib.util
import logging
from collections.abc import Iterator, Mapping
from typing import Any, Optional
from typing import Any
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import BaseLLM
@@ -71,13 +71,13 @@ class HuggingFacePipeline(BaseLLM):
"""
pipeline: Any = None #: :meta private:
model_id: Optional[str] = None
model_id: str | None = None
"""The model name. If not set explicitly by the user,
it will be inferred from the provided pipeline (if available).
If neither is provided, the DEFAULT_MODEL_ID will be used."""
model_kwargs: Optional[dict] = None
model_kwargs: dict | None = None
"""Keyword arguments passed to the model."""
pipeline_kwargs: Optional[dict] = None
pipeline_kwargs: dict | None = None
"""Keyword arguments passed to the pipeline."""
batch_size: int = DEFAULT_BATCH_SIZE
"""Batch size to use when passing multiple documents to generate."""
@@ -103,10 +103,10 @@ class HuggingFacePipeline(BaseLLM):
model_id: str,
task: str,
backend: str = "default",
device: Optional[int] = None,
device_map: Optional[str] = None,
model_kwargs: Optional[dict] = None,
pipeline_kwargs: Optional[dict] = None,
device: int | None = None,
device_map: str | None = None,
model_kwargs: dict | None = None,
pipeline_kwargs: dict | None = None,
batch_size: int = DEFAULT_BATCH_SIZE,
**kwargs: Any,
) -> HuggingFacePipeline:
@@ -311,8 +311,8 @@ class HuggingFacePipeline(BaseLLM):
def _generate(
self,
prompts: list[str],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> LLMResult:
# List to hold all results
@@ -363,8 +363,8 @@ class HuggingFacePipeline(BaseLLM):
def _stream(
self,
prompt: str,
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
from threading import Thread

3
libs/partners/huggingface/langchain_huggingface/utils/import_utils.py
View File

@@ -3,7 +3,6 @@ from __future__ import annotations
import importlib.metadata
import importlib.util
import operator as op
from typing import Union
from packaging import version
@@ -44,7 +43,7 @@ _openvino_available = importlib.util.find_spec("openvino") is not None
# This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L319
def compare_versions(
library_or_version: Union[str, version.Version],
library_or_version: str | version.Version,
operation: str,
requirement_version: str,
) -> bool: