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groq[patch]: ruff fixes and rules (#31904)

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* bump ruff deps
* add more thorough ruff rules
* fix said rules
This commit is contained in:
Mason Daugherty 2025-07-08 10:25:46 -04:00 committed by GitHub
parent 750721b4c3
commit dd76209bbd
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9 changed files with 119 additions and 52 deletions
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85
libs/partners/groq/langchain_groq/chat_models.py
View File

@ -83,7 +83,7 @@ from langchain_groq.version import __version__
class ChatGroq(BaseChatModel):
"""Groq Chat large language models API.
r"""Groq Chat large language models API.
To use, you should have the
environment variable ``GROQ_API_KEY`` set with your API key.
@ -412,7 +412,8 @@ class ChatGroq(BaseChatModel):
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
msg = f"Found {field_name} supplied twice."
raise ValueError(msg)
if field_name not in all_required_field_names:
warnings.warn(
f"""WARNING! {field_name} is not default parameter.
@ -423,10 +424,11 @@ class ChatGroq(BaseChatModel):
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
msg = (
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
raise ValueError(msg)
values["model_kwargs"] = extra
return values
@ -435,9 +437,11 @@ class ChatGroq(BaseChatModel):
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
if self.n < 1:
raise ValueError("n must be at least 1.")
msg = "n must be at least 1."
raise ValueError(msg)
if self.n > 1 and self.streaming:
raise ValueError("n must be 1 when streaming.")
msg = "n must be 1 when streaming."
raise ValueError(msg)
if self.temperature == 0:
self.temperature = 1e-8
@ -470,10 +474,11 @@ class ChatGroq(BaseChatModel):
**client_params, **async_specific
).chat.completions
except ImportError as exc:
raise ImportError(
msg = (
"Could not import groq python package. "
"Please install it with `pip install groq`."
) from exc
)
raise ImportError(msg) from exc
return self
#
@ -680,7 +685,7 @@ class ChatGroq(BaseChatModel):
return params
def _create_chat_result(
self, response: Union[dict, BaseModel], params: dict
self, response: dict | BaseModel, params: dict
) -> ChatResult:
generations = []
if not isinstance(response, dict):
@ -698,7 +703,7 @@ class ChatGroq(BaseChatModel):
"total_tokens", input_tokens + output_tokens
),
}
generation_info = dict(finish_reason=res.get("finish_reason"))
generation_info = {"finish_reason": res.get("finish_reason")}
if "logprobs" in res:
generation_info["logprobs"] = res["logprobs"]
gen = ChatGeneration(
@ -755,7 +760,7 @@ class ChatGroq(BaseChatModel):
self,
functions: Sequence[Union[dict[str, Any], type[BaseModel], Callable, BaseTool]],
function_call: Optional[
Union[_FunctionCall, str, Literal["auto", "none"]]
Union[_FunctionCall, str, Literal["auto", "none"]] # noqa: PYI051
] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
@ -777,8 +782,8 @@ class ChatGroq(BaseChatModel):
(if any).
**kwargs: Any additional parameters to pass to
:meth:`~langchain_groq.chat_models.ChatGroq.bind`.
"""
"""
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
if function_call is not None:
function_call = (
@ -788,18 +793,20 @@ class ChatGroq(BaseChatModel):
else function_call
)
if isinstance(function_call, dict) and len(formatted_functions) != 1:
raise ValueError(
msg = (
"When specifying `function_call`, you must provide exactly one "
"function."
)
raise ValueError(msg)
if (
isinstance(function_call, dict)
and formatted_functions[0]["name"] != function_call["name"]
):
raise ValueError(
msg = (
f"Function call {function_call} was specified, but the only "
f"provided function was {formatted_functions[0]['name']}."
)
raise ValueError(msg)
kwargs = {**kwargs, "function_call": function_call}
return super().bind(
functions=formatted_functions,
@ -811,7 +818,7 @@ class ChatGroq(BaseChatModel):
tools: Sequence[Union[dict[str, Any], type[BaseModel], Callable, BaseTool]],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "any", "none"], bool]
Union[dict, str, Literal["auto", "any", "none"], bool] # noqa: PYI051
] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
@ -829,8 +836,8 @@ class ChatGroq(BaseChatModel):
{"type": "function", "function": {"name": <<tool_name>>}}.
**kwargs: Any additional parameters to pass to the
:class:`~langchain.runnable.Runnable` constructor.
"""
"""
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
if tool_choice is not None and tool_choice:
if tool_choice == "any":
@ -841,10 +848,11 @@ class ChatGroq(BaseChatModel):
tool_choice = {"type": "function", "function": {"name": tool_choice}}
if isinstance(tool_choice, bool):
if len(tools) > 1:
raise ValueError(
msg = (
"tool_choice can only be True when there is one tool. Received "
f"{len(tools)} tools."
)
raise ValueError(msg)
tool_name = formatted_tools[0]["function"]["name"]
tool_choice = {
"type": "function",
@ -861,8 +869,8 @@ class ChatGroq(BaseChatModel):
method: Literal["function_calling", "json_mode"] = "function_calling",
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
"""Model wrapper that returns outputs formatted to match the given schema.
) -> Runnable[LanguageModelInput, dict | BaseModel]:
r"""Model wrapper that returns outputs formatted to match the given schema.
Args:
schema:
@ -895,6 +903,9 @@ class ChatGroq(BaseChatModel):
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
kwargs:
Any additional parameters to pass to the
:class:`~langchain.runnable.Runnable` constructor.
Returns:
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
@ -1075,10 +1086,12 @@ class ChatGroq(BaseChatModel):
# },
# 'parsing_error': None
# }
""" # noqa: E501
_ = kwargs.pop("strict", None)
if kwargs:
raise ValueError(f"Received unsupported arguments {kwargs}")
msg = f"Received unsupported arguments {kwargs}"
raise ValueError(msg)
is_pydantic_schema = _is_pydantic_class(schema)
if method == "json_schema":
# Some applications require that incompatible parameters (e.g., unsupported
@ -1086,10 +1099,11 @@ class ChatGroq(BaseChatModel):
method = "function_calling"
if method == "function_calling":
if schema is None:
raise ValueError(
msg = (
"schema must be specified when method is 'function_calling'. "
"Received None."
)
raise ValueError(msg)
formatted_tool = convert_to_openai_tool(schema)
tool_name = formatted_tool["function"]["name"]
llm = self.bind_tools(
@ -1123,10 +1137,11 @@ class ChatGroq(BaseChatModel):
else JsonOutputParser()
)
else:
raise ValueError(
msg = (
f"Unrecognized method argument. Expected one of 'function_calling' or "
f"'json_mode'. Received: '{method}'"
)
raise ValueError(msg)
if include_raw:
parser_assign = RunnablePassthrough.assign(
@ -1137,7 +1152,6 @@ class ChatGroq(BaseChatModel):
[parser_none], exception_key="parsing_error"
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser
@ -1160,6 +1174,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
Returns:
The dictionary.
"""
message_dict: dict[str, Any]
if isinstance(message, ChatMessage):
@ -1200,7 +1215,8 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
"tool_call_id": message.tool_call_id,
}
else:
raise TypeError(f"Got unknown type {message}")
msg = f"Got unknown type {message}"
raise TypeError(msg)
if "name" in message.additional_kwargs:
message_dict["name"] = message.additional_kwargs["name"]
return message_dict
@ -1224,7 +1240,7 @@ def _convert_chunk_to_message_chunk(
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
if role == "assistant" or default_class == AIMessageChunk:
if reasoning := _dict.get("reasoning"):
additional_kwargs["reasoning_content"] = reasoning
if usage := (chunk.get("x_groq") or {}).get("usage"):
@ -1242,16 +1258,15 @@ def _convert_chunk_to_message_chunk(
additional_kwargs=additional_kwargs,
usage_metadata=usage_metadata, # type: ignore[arg-type]
)
elif role == "system" or default_class == SystemMessageChunk:
if role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content)
elif role == "function" or default_class == FunctionMessageChunk:
if role == "function" or default_class == FunctionMessageChunk:
return FunctionMessageChunk(content=content, name=_dict["name"])
elif role == "tool" or default_class == ToolMessageChunk:
if role == "tool" or default_class == ToolMessageChunk:
return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
elif role or default_class == ChatMessageChunk:
if role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role)
else:
return default_class(content=content) # type: ignore
return default_class(content=content) # type: ignore[call-arg]
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
@ -1262,12 +1277,13 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
Returns:
The LangChain message.
"""
id_ = _dict.get("id")
role = _dict.get("role")
if role == "user":
return HumanMessage(content=_dict.get("content", ""))
elif role == "assistant":
if role == "assistant":
content = _dict.get("content", "") or ""
additional_kwargs: dict = {}
if reasoning := _dict.get("reasoning"):
@ -1292,11 +1308,11 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls,
)
elif role == "system":
if role == "system":
return SystemMessage(content=_dict.get("content", ""))
elif role == "function":
if role == "function":
return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name")) # type: ignore[arg-type]
elif role == "tool":
if role == "tool":
additional_kwargs = {}
if "name" in _dict:
additional_kwargs["name"] = _dict["name"]
@ -1305,7 +1321,6 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
tool_call_id=_dict.get("tool_call_id"),
additional_kwargs=additional_kwargs,
)
else:
return ChatMessage(content=_dict.get("content", ""), role=role) # type: ignore[arg-type]

54
libs/partners/groq/pyproject.toml
View File

@ -44,8 +44,58 @@ disallow_untyped_defs = "True"
target-version = "py39"
[tool.ruff.lint]
select = ["E", "F", "I", "W", "UP", "S"]
ignore = [ "UP007", ]
select = [
"A", # flake8-builtins
"ASYNC", # flake8-async
"C4", # flake8-comprehensions
"COM", # flake8-commas
"D", # pydocstyle
"DOC", # pydoclint
"E", # pycodestyle error
"EM", # flake8-errmsg
"F", # pyflakes
"FA", # flake8-future-annotations
"FBT", # flake8-boolean-trap
"FLY", # flake8-flynt
"I", # isort
"ICN", # flake8-import-conventions
"INT", # flake8-gettext
"ISC", # isort-comprehensions
"PGH", # pygrep-hooks
"PIE", # flake8-pie
"PERF", # flake8-perf
"PYI", # flake8-pyi
"Q", # flake8-quotes
"RET", # flake8-return
"RSE", # flake8-rst-docstrings
"RUF", # ruff
"S", # flake8-bandit
"SLF", # flake8-self
"SLOT", # flake8-slots
"SIM", # flake8-simplify
"T10", # flake8-debugger
"T20", # flake8-print
"TID", # flake8-tidy-imports
"UP", # pyupgrade
"W", # pycodestyle warning
"YTT", # flake8-2020
]
ignore = [
"D100", # Missing docstring in public module
"D101", # Missing docstring in public class
"D102", # Missing docstring in public method
"D103", # Missing docstring in public function
"D104", # Missing docstring in public package
"D105", # Missing docstring in magic method
"D107", # Missing docstring in __init__
"COM812", # Messes with the formatter
"ISC001", # Messes with the formatter
"PERF203", # Rarely useful
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
]
[tool.coverage.run]
omit = ["tests/*"]

2
libs/partners/groq/scripts/check_imports.py
View File

@ -10,8 +10,6 @@ if __name__ == "__main__":
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(file)
traceback.print_exc()
print()
sys.exit(1 if has_failure else 0)

11
libs/partners/groq/tests/integration_tests/test_chat_models.py
View File

@ -1,5 +1,7 @@
"""Test ChatGroq chat model."""
from __future__ import annotations
import json
from typing import Any, Optional, cast
@ -109,11 +111,12 @@ async def test_astream() -> None:
if token.response_metadata:
chunks_with_response_metadata += 1
if chunks_with_token_counts != 1 or chunks_with_response_metadata != 1:
raise AssertionError(
msg = (
"Expected exactly one chunk with token counts or metadata. "
"AIMessageChunk aggregation adds / appends these metadata. Check that "
"this is behaving properly."
)
raise AssertionError(msg)
assert isinstance(full, AIMessageChunk)
assert full.usage_metadata is not None
assert full.usage_metadata["input_tokens"] > 0
@ -451,7 +454,7 @@ async def test_astreaming_tool_call() -> None:
@pytest.mark.scheduled
def test_json_mode_structured_output() -> None:
"""Test with_structured_output with json"""
"""Test with_structured_output with json."""
class Joke(BaseModel):
"""Joke to tell user."""
@ -496,9 +499,9 @@ def test_setting_service_tier_class() -> None:
assert response.response_metadata.get("service_tier") == "on_demand"
with pytest.raises(ValueError):
ChatGroq(model=MODEL_NAME, service_tier=None) # type: ignore
ChatGroq(model=MODEL_NAME, service_tier=None) # type: ignore[arg-type]
with pytest.raises(ValueError):
ChatGroq(model=MODEL_NAME, service_tier="invalid") # type: ignore
ChatGroq(model=MODEL_NAME, service_tier="invalid") # type: ignore[arg-type]
def test_setting_service_tier_request() -> None:

1
libs/partners/groq/tests/integration_tests/test_compile.py
View File

@ -4,4 +4,3 @@ import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass

2
libs/partners/groq/tests/integration_tests/test_standard.py
View File

@ -1,4 +1,4 @@
"""Standard LangChain interface tests"""
"""Standard LangChain interface tests."""
import pytest
from langchain_core.language_models import BaseChatModel

6
libs/partners/groq/tests/unit_tests/fake/callbacks.py
View File

@ -1,5 +1,7 @@
"""A fake callback handler for testing purposes."""
from __future__ import annotations
from itertools import chain
from typing import Any, Optional, Union
from uuid import UUID
@ -257,7 +259,7 @@ class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
self.on_retriever_error_common()
# Overriding since BaseModel has __deepcopy__ method as well
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore
def __deepcopy__(self, memo: dict) -> FakeCallbackHandler: # type: ignore[override]
return self
@ -392,5 +394,5 @@ class FakeAsyncCallbackHandler(AsyncCallbackHandler, BaseFakeCallbackHandlerMixi
self.on_text_common()
# Overriding since BaseModel has __deepcopy__ method as well
def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore
def __deepcopy__(self, memo: dict) -> FakeAsyncCallbackHandler: # type: ignore[override]
return self

4
libs/partners/groq/tests/unit_tests/test_chat_models.py
View File

@ -244,7 +244,7 @@ def test_chat_groq_invalid_streaming_params() -> None:
def test_chat_groq_secret() -> None:
"""Test that secret is not printed"""
"""Test that secret is not printed."""
secret = "secretKey" # noqa: S105
not_secret = "safe" # noqa: S105
llm = ChatGroq(model="foo", api_key=secret, model_kwargs={"not_secret": not_secret}) # type: ignore[call-arg, arg-type]
@ -255,7 +255,7 @@ def test_chat_groq_secret() -> None:
@pytest.mark.filterwarnings("ignore:The function `loads` is in beta")
def test_groq_serialization() -> None:
"""Test that ChatGroq can be successfully serialized and deserialized"""
"""Test that ChatGroq can be successfully serialized and deserialized."""
api_key1 = "top secret"
api_key2 = "topest secret"
llm = ChatGroq(model="foo", api_key=api_key1, temperature=0.5) # type: ignore[call-arg, arg-type]

2
libs/partners/groq/tests/unit_tests/test_standard.py
View File

@ -1,4 +1,4 @@
"""Standard LangChain interface tests"""
"""Standard LangChain interface tests."""
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests.chat_models import (