langchain: Add ruff rules A (#31888)

See https://docs.astral.sh/ruff/rules/#flake8-builtins-a

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>

libs/langchain/langchain/agents/agent.py

@@ -41,7 +41,7 @@ from langchain_core.runnables.utils import AddableDict
 from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
 from pydantic import BaseModel, ConfigDict, model_validator
-from typing_extensions import Self
+from typing_extensions import Self, override
 
 from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
 from langchain.agents.agent_iterator import AgentExecutorIterator
@@ -1749,6 +1749,7 @@ class AgentExecutor(Chain):
         else:
             return intermediate_steps
 
+    @override
     def stream(
         self,
         input: Union[dict[str, Any], Any],
@@ -1779,6 +1780,7 @@ class AgentExecutor(Chain):
         )
         yield from iterator
 
+    @override
     async def astream(
         self,
         input: Union[dict[str, Any], Any],

libs/langchain/langchain/agents/openai_assistant/base.py

@@ -20,7 +20,7 @@ from langchain_core.runnables import RunnableConfig, RunnableSerializable, ensur
 from langchain_core.tools import BaseTool
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from pydantic import BaseModel, Field, model_validator
-from typing_extensions import Self
+from typing_extensions import Self, override
 
 if TYPE_CHECKING:
     import openai
@@ -272,6 +272,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
         )
         return cls(assistant_id=assistant.id, client=client, **kwargs)
 
+    @override
     def invoke(
         self, input: dict, config: Optional[RunnableConfig] = None, **kwargs: Any
     ) -> OutputType:
@@ -399,6 +400,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
         )
         return cls(assistant_id=assistant.id, async_client=async_client, **kwargs)
 
+    @override
     async def ainvoke(
         self, input: dict, config: Optional[RunnableConfig] = None, **kwargs: Any
     ) -> OutputType:
@@ -515,10 +517,10 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
         }
         return submit_tool_outputs
 
-    def _create_run(self, input: dict) -> Any:
+    def _create_run(self, input_dict: dict) -> Any:
         params = {
             k: v
-            for k, v in input.items()
+            for k, v in input_dict.items()
             if k
             in (
                 "instructions",
@@ -534,15 +536,15 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
             )
         }
         return self.client.beta.threads.runs.create(
-            input["thread_id"],
+            input_dict["thread_id"],
             assistant_id=self.assistant_id,
             **params,
         )
 
-    def _create_thread_and_run(self, input: dict, thread: dict) -> Any:
+    def _create_thread_and_run(self, input_dict: dict, thread: dict) -> Any:
         params = {
             k: v
-            for k, v in input.items()
+            for k, v in input_dict.items()
             if k
             in (
                 "instructions",
@@ -673,10 +675,10 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
         }
         return submit_tool_outputs
 
-    async def _acreate_run(self, input: dict) -> Any:
+    async def _acreate_run(self, input_dict: dict) -> Any:
         params = {
             k: v
-            for k, v in input.items()
+            for k, v in input_dict.items()
             if k
             in (
                 "instructions",
@@ -692,15 +694,15 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
             )
         }
         return await self.async_client.beta.threads.runs.create(
-            input["thread_id"],
+            input_dict["thread_id"],
             assistant_id=self.assistant_id,
             **params,
         )
 
-    async def _acreate_thread_and_run(self, input: dict, thread: dict) -> Any:
+    async def _acreate_thread_and_run(self, input_dict: dict, thread: dict) -> Any:
         params = {
             k: v
-            for k, v in input.items()
+            for k, v in input_dict.items()
             if k
             in (
                 "instructions",

libs/langchain/langchain/chains/base.py

@@ -35,6 +35,7 @@ from pydantic import (
     field_validator,
     model_validator,
 )
+from typing_extensions import override
 
 from langchain.schema import RUN_KEY
 
@@ -118,6 +119,7 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
         # This is correct, but pydantic typings/mypy don't think so.
         return create_model("ChainOutput", **{k: (Any, None) for k in self.output_keys})
 
+    @override
     def invoke(
         self,
         input: dict[str, Any],
@@ -171,6 +173,7 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
             final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
         return final_outputs
 
+    @override
     async def ainvoke(
         self,
         input: dict[str, Any],

libs/langchain/langchain/chains/query_constructor/base.py

@@ -88,9 +88,11 @@ class StructuredQueryOutputParser(BaseOutputParser[StructuredQuery]):
         if fix_invalid:
 
             def ast_parse(raw_filter: str) -> Optional[FilterDirective]:
-                filter = cast(Optional[FilterDirective], get_parser().parse(raw_filter))
+                filter_directive = cast(
+                    Optional[FilterDirective], get_parser().parse(raw_filter)
+                )
                 fixed = fix_filter_directive(
-                    filter,
+                    filter_directive,
                     allowed_comparators=allowed_comparators,
                     allowed_operators=allowed_operators,
                     allowed_attributes=allowed_attributes,
@@ -107,7 +109,7 @@ class StructuredQueryOutputParser(BaseOutputParser[StructuredQuery]):
 
 
 def fix_filter_directive(
-    filter: Optional[FilterDirective],
+    filter: Optional[FilterDirective],  # noqa: A002
     *,
     allowed_comparators: Optional[Sequence[Comparator]] = None,
     allowed_operators: Optional[Sequence[Operator]] = None,

libs/langchain/langchain/chat_models/base.py

@@ -28,7 +28,7 @@ from langchain_core.runnables.schema import StreamEvent
 from langchain_core.tools import BaseTool
 from langchain_core.tracers import RunLog, RunLogPatch
 from pydantic import BaseModel
-from typing_extensions import TypeAlias
+from typing_extensions import TypeAlias, override
 
 __all__ = [
     "init_chat_model",
@@ -673,6 +673,7 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
             list[AnyMessage],
         ]
 
+    @override
     def invoke(
         self,
         input: LanguageModelInput,
@@ -681,6 +682,7 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
     ) -> Any:
         return self._model(config).invoke(input, config=config, **kwargs)
 
+    @override
     async def ainvoke(
         self,
         input: LanguageModelInput,
@@ -689,6 +691,7 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
     ) -> Any:
         return await self._model(config).ainvoke(input, config=config, **kwargs)
 
+    @override
     def stream(
         self,
         input: LanguageModelInput,
@@ -697,6 +700,7 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
     ) -> Iterator[Any]:
         yield from self._model(config).stream(input, config=config, **kwargs)
 
+    @override
     async def astream(
         self,
         input: LanguageModelInput,
@@ -802,6 +806,7 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
             ):
                 yield x
 
+    @override
     def transform(
         self,
         input: Iterator[LanguageModelInput],
@@ -810,6 +815,7 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
     ) -> Iterator[Any]:
         yield from self._model(config).transform(input, config=config, **kwargs)
 
+    @override
     async def atransform(
         self,
         input: AsyncIterator[LanguageModelInput],
@@ -853,6 +859,7 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
         **kwargs: Any,
     ) -> AsyncIterator[RunLog]: ...
 
+    @override
     async def astream_log(
         self,
         input: Any,
@@ -883,6 +890,7 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
         ):
             yield x
 
+    @override
     async def astream_events(
         self,
         input: Any,

libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py

@@ -27,6 +27,7 @@ from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.tools import BaseTool
 from pydantic import ConfigDict, Field
+from typing_extensions import override
 
 from langchain.chains.llm import LLMChain
 from langchain.evaluation.agents.trajectory_eval_prompt import (
@@ -326,6 +327,7 @@ The following is the expected answer. Use this to measure correctness:
         )
         return cast(dict, self.output_parser.parse(raw_output))
 
+    @override
     def _evaluate_agent_trajectory(
         self,
         *,
@@ -368,6 +370,7 @@ The following is the expected answer. Use this to measure correctness:
             return_only_outputs=True,
         )
 
+    @override
     async def _aevaluate_agent_trajectory(
         self,
         *,

libs/langchain/langchain/evaluation/comparison/eval_chain.py

@@ -11,6 +11,7 @@ from langchain_core.language_models import BaseLanguageModel
 from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.prompts.prompt import PromptTemplate
 from pydantic import ConfigDict, Field
+from typing_extensions import override
 
 from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
 from langchain.chains.llm import LLMChain
@@ -278,7 +279,7 @@ Performance may be significantly worse with other models."
         self,
         prediction: str,
         prediction_b: str,
-        input: Optional[str],
+        input_: Optional[str],
         reference: Optional[str],
     ) -> dict:
         """Prepare the input for the chain.
@@ -286,21 +287,21 @@ Performance may be significantly worse with other models."
         Args:
             prediction (str): The output string from the first model.
             prediction_b (str): The output string from the second model.
-            input (str, optional): The input or task string.
+            input_ (str, optional): The input or task string.
             reference (str, optional): The reference string, if any.
 
         Returns:
             dict: The prepared input for the chain.
 
         """
-        input_ = {
+        input_dict = {
             "prediction": prediction,
             "prediction_b": prediction_b,
-            "input": input,
+            "input": input_,
         }
         if self.requires_reference:
-            input_["reference"] = reference
+            input_dict["reference"] = reference
-        return input_
+        return input_dict
 
     def _prepare_output(self, result: dict) -> dict:
         """Prepare the output."""
@@ -309,6 +310,7 @@ Performance may be significantly worse with other models."
             parsed[RUN_KEY] = result[RUN_KEY]
         return parsed
 
+    @override
     def _evaluate_string_pairs(
         self,
         *,
@@ -351,6 +353,7 @@ Performance may be significantly worse with other models."
         )
         return self._prepare_output(result)
 
+    @override
     async def _aevaluate_string_pairs(
         self,
         *,

libs/langchain/langchain/evaluation/criteria/eval_chain.py

@@ -10,6 +10,7 @@ from langchain_core.language_models import BaseLanguageModel
 from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.prompts import BasePromptTemplate
 from pydantic import ConfigDict, Field
+from typing_extensions import override
 
 from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
 from langchain.chains.llm import LLMChain
@@ -383,16 +384,16 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
         self,
         prediction: str,
         reference: Optional[str],
-        input: Optional[str],
+        input_: Optional[str],
     ) -> dict:
         """Get the evaluation input."""
-        input_ = {
+        input_dict = {
-            "input": input,
+            "input": input_,
             "output": prediction,
         }
         if self.requires_reference:
-            input_["reference"] = reference
+            input_dict["reference"] = reference
-        return input_
+        return input_dict
 
     def _prepare_output(self, result: dict) -> dict:
         """Prepare the output."""
@@ -401,6 +402,7 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
             parsed[RUN_KEY] = result[RUN_KEY]
         return parsed
 
+    @override
     def _evaluate_strings(
         self,
         *,
@@ -456,6 +458,7 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
         )
         return self._prepare_output(result)
 
+    @override
     async def _aevaluate_strings(
         self,
         *,

libs/langchain/langchain/evaluation/parsing/base.py

@@ -5,6 +5,7 @@ from operator import eq
 from typing import Any, Callable, Optional, Union, cast
 
 from langchain_core.utils.json import parse_json_markdown
+from typing_extensions import override
 
 from langchain.evaluation.schema import StringEvaluator
 
@@ -49,6 +50,7 @@ class JsonValidityEvaluator(StringEvaluator):
     def evaluation_name(self) -> str:
         return "json_validity"
 
+    @override
     def _evaluate_strings(
         self,
         prediction: str,
@@ -132,6 +134,7 @@ class JsonEqualityEvaluator(StringEvaluator):
             return parse_json_markdown(string)
         return string
 
+    @override
     def _evaluate_strings(
         self,
         prediction: str,

libs/langchain/langchain/evaluation/parsing/json_distance.py

@@ -2,6 +2,7 @@ import json
 from typing import Any, Callable, Optional, Union
 
 from langchain_core.utils.json import parse_json_markdown
+from typing_extensions import override
 
 from langchain.evaluation.schema import StringEvaluator
 
@@ -82,6 +83,7 @@ class JsonEditDistanceEvaluator(StringEvaluator):
             return parse_json_markdown(node)
         return node
 
+    @override
     def _evaluate_strings(
         self,
         prediction: str,

libs/langchain/langchain/evaluation/parsing/json_schema.py

@@ -1,6 +1,7 @@
 from typing import Any, Union
 
 from langchain_core.utils.json import parse_json_markdown
+from typing_extensions import override
 
 from langchain.evaluation.schema import StringEvaluator
 
@@ -85,6 +86,7 @@ class JsonSchemaEvaluator(StringEvaluator):
         except ValidationError as e:
             return {"score": False, "reasoning": repr(e)}
 
+    @override
     def _evaluate_strings(
         self,
         prediction: Union[str, Any],

libs/langchain/langchain/evaluation/qa/eval_chain.py

@@ -11,6 +11,7 @@ from langchain_core.callbacks import Callbacks
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import PromptTemplate
 from pydantic import ConfigDict
+from typing_extensions import override
 
 from langchain.chains.llm import LLMChain
 from langchain.evaluation.qa.eval_prompt import CONTEXT_PROMPT, COT_PROMPT, PROMPT
@@ -154,6 +155,7 @@ class QAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
             parsed_result[RUN_KEY] = result[RUN_KEY]
         return parsed_result
 
+    @override
     def _evaluate_strings(
         self,
         *,
@@ -189,6 +191,7 @@ class QAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
         )
         return self._prepare_output(result)
 
+    @override
     async def _aevaluate_strings(
         self,
         *,
@@ -296,6 +299,7 @@ class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
             parsed_result[RUN_KEY] = result[RUN_KEY]
         return parsed_result
 
+    @override
     def _evaluate_strings(
         self,
         *,
@@ -317,6 +321,7 @@ class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
         )
         return self._prepare_output(result)
 
+    @override
     async def _aevaluate_strings(
         self,
         *,

libs/langchain/langchain/evaluation/schema.py

@@ -109,13 +109,13 @@ class _EvalArgsMixin:
     def _check_evaluation_args(
         self,
         reference: Optional[str] = None,
-        input: Optional[str] = None,
+        input_: Optional[str] = None,
     ) -> None:
         """Check if the evaluation arguments are valid.
 
         Args:
             reference (Optional[str], optional): The reference label.
-            input (Optional[str], optional): The input string.
+            input_ (Optional[str], optional): The input string.
         Raises:
             ValueError: If the evaluator requires an input string but none is provided,
                 or if the evaluator requires a reference label but none is provided.
@@ -152,7 +152,7 @@ class StringEvaluator(_EvalArgsMixin, ABC):
         *,
         prediction: Union[str, Any],
         reference: Optional[Union[str, Any]] = None,
-        input: Optional[Union[str, Any]] = None,
+        input: Optional[Union[str, Any]] = None,  # noqa: A002
         **kwargs: Any,
     ) -> dict:
         """Evaluate Chain or LLM output, based on optional input and label.
@@ -175,7 +175,7 @@ class StringEvaluator(_EvalArgsMixin, ABC):
         *,
         prediction: Union[str, Any],
         reference: Optional[Union[str, Any]] = None,
-        input: Optional[Union[str, Any]] = None,
+        input: Optional[Union[str, Any]] = None,  # noqa: A002
         **kwargs: Any,
     ) -> dict:
         """Asynchronously evaluate Chain or LLM output, based on optional input and label.
@@ -206,7 +206,7 @@ class StringEvaluator(_EvalArgsMixin, ABC):
         *,
         prediction: str,
         reference: Optional[str] = None,
-        input: Optional[str] = None,
+        input: Optional[str] = None,  # noqa: A002
         **kwargs: Any,
     ) -> dict:
         """Evaluate Chain or LLM output, based on optional input and label.
@@ -219,7 +219,7 @@ class StringEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: The evaluation results containing the score or value.
         """  # noqa: E501
-        self._check_evaluation_args(reference=reference, input=input)
+        self._check_evaluation_args(reference=reference, input_=input)
         return self._evaluate_strings(
             prediction=prediction, reference=reference, input=input, **kwargs
         )
@@ -229,7 +229,7 @@ class StringEvaluator(_EvalArgsMixin, ABC):
         *,
         prediction: str,
         reference: Optional[str] = None,
-        input: Optional[str] = None,
+        input: Optional[str] = None,  # noqa: A002
         **kwargs: Any,
     ) -> dict:
         """Asynchronously evaluate Chain or LLM output, based on optional input and label.
@@ -242,7 +242,7 @@ class StringEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: The evaluation results containing the score or value.
         """  # noqa: E501
-        self._check_evaluation_args(reference=reference, input=input)
+        self._check_evaluation_args(reference=reference, input_=input)
         return await self._aevaluate_strings(
             prediction=prediction, reference=reference, input=input, **kwargs
         )
@@ -258,7 +258,7 @@ class PairwiseStringEvaluator(_EvalArgsMixin, ABC):
         prediction: str,
         prediction_b: str,
         reference: Optional[str] = None,
-        input: Optional[str] = None,
+        input: Optional[str] = None,  # noqa: A002
         **kwargs: Any,
     ) -> dict:
         """Evaluate the output string pairs.
@@ -279,7 +279,7 @@ class PairwiseStringEvaluator(_EvalArgsMixin, ABC):
         prediction: str,
         prediction_b: str,
         reference: Optional[str] = None,
-        input: Optional[str] = None,
+        input: Optional[str] = None,  # noqa: A002
         **kwargs: Any,
     ) -> dict:
         """Asynchronously evaluate the output string pairs.
@@ -309,7 +309,7 @@ class PairwiseStringEvaluator(_EvalArgsMixin, ABC):
         prediction: str,
         prediction_b: str,
         reference: Optional[str] = None,
-        input: Optional[str] = None,
+        input: Optional[str] = None,  # noqa: A002
         **kwargs: Any,
     ) -> dict:
         """Evaluate the output string pairs.
@@ -323,7 +323,7 @@ class PairwiseStringEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: A dictionary containing the preference, scores, and/or other information.
         """  # noqa: E501
-        self._check_evaluation_args(reference=reference, input=input)
+        self._check_evaluation_args(reference=reference, input_=input)
         return self._evaluate_string_pairs(
             prediction=prediction,
             prediction_b=prediction_b,
@@ -338,7 +338,7 @@ class PairwiseStringEvaluator(_EvalArgsMixin, ABC):
         prediction: str,
         prediction_b: str,
         reference: Optional[str] = None,
-        input: Optional[str] = None,
+        input: Optional[str] = None,  # noqa: A002
         **kwargs: Any,
     ) -> dict:
         """Asynchronously evaluate the output string pairs.
@@ -352,7 +352,7 @@ class PairwiseStringEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: A dictionary containing the preference, scores, and/or other information.
         """  # noqa: E501
-        self._check_evaluation_args(reference=reference, input=input)
+        self._check_evaluation_args(reference=reference, input_=input)
         return await self._aevaluate_string_pairs(
             prediction=prediction,
             prediction_b=prediction_b,
@@ -376,7 +376,7 @@ class AgentTrajectoryEvaluator(_EvalArgsMixin, ABC):
         *,
         prediction: str,
         agent_trajectory: Sequence[tuple[AgentAction, str]],
-        input: str,
+        input: str,  # noqa: A002
         reference: Optional[str] = None,
         **kwargs: Any,
     ) -> dict:
@@ -398,7 +398,7 @@ class AgentTrajectoryEvaluator(_EvalArgsMixin, ABC):
         *,
         prediction: str,
         agent_trajectory: Sequence[tuple[AgentAction, str]],
-        input: str,
+        input: str,  # noqa: A002
         reference: Optional[str] = None,
         **kwargs: Any,
     ) -> dict:
@@ -429,7 +429,7 @@ class AgentTrajectoryEvaluator(_EvalArgsMixin, ABC):
         *,
         prediction: str,
         agent_trajectory: Sequence[tuple[AgentAction, str]],
-        input: str,
+        input: str,  # noqa: A002
         reference: Optional[str] = None,
         **kwargs: Any,
     ) -> dict:
@@ -445,7 +445,7 @@ class AgentTrajectoryEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: The evaluation result.
         """
-        self._check_evaluation_args(reference=reference, input=input)
+        self._check_evaluation_args(reference=reference, input_=input)
         return self._evaluate_agent_trajectory(
             prediction=prediction,
             input=input,
@@ -459,7 +459,7 @@ class AgentTrajectoryEvaluator(_EvalArgsMixin, ABC):
         *,
         prediction: str,
         agent_trajectory: Sequence[tuple[AgentAction, str]],
-        input: str,
+        input: str,  # noqa: A002
         reference: Optional[str] = None,
         **kwargs: Any,
     ) -> dict:
@@ -475,7 +475,7 @@ class AgentTrajectoryEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: The evaluation result.
         """
-        self._check_evaluation_args(reference=reference, input=input)
+        self._check_evaluation_args(reference=reference, input_=input)
         return await self._aevaluate_agent_trajectory(
             prediction=prediction,
             input=input,

libs/langchain/langchain/evaluation/scoring/eval_chain.py

@@ -11,6 +11,7 @@ from langchain_core.language_models import BaseLanguageModel
 from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.prompts.prompt import PromptTemplate
 from pydantic import ConfigDict, Field
+from typing_extensions import override
 
 from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
 from langchain.chains.llm import LLMChain
@@ -292,7 +293,7 @@ Performance may be significantly worse with other models."
     def _prepare_input(
         self,
         prediction: str,
-        input: Optional[str],
+        input_: Optional[str],
         reference: Optional[str],
     ) -> dict:
         """Prepare the input for the chain.
@@ -300,20 +301,20 @@ Performance may be significantly worse with other models."
         Args:
             prediction (str): The output string from the first model.
             prediction_b (str): The output string from the second model.
-            input (str, optional): The input or task string.
+            input_ (str, optional): The input or task string.
             reference (str, optional): The reference string, if any.
 
         Returns:
             dict: The prepared input for the chain.
 
         """
-        input_ = {
+        input_dict = {
             "prediction": prediction,
-            "input": input,
+            "input": input_,
         }
         if self.requires_reference:
-            input_["reference"] = reference
+            input_dict["reference"] = reference
-        return input_
+        return input_dict
 
     def _prepare_output(self, result: dict) -> dict:
         """Prepare the output."""
@@ -324,6 +325,7 @@ Performance may be significantly worse with other models."
             parsed["score"] = parsed["score"] / self.normalize_by
         return parsed
 
+    @override
     def _evaluate_strings(
         self,
         *,
@@ -361,7 +363,8 @@ Performance may be significantly worse with other models."
         )
         return self._prepare_output(result)
 
-    async def _aevaluate_string_pairs(
+    @override
+    async def _aevaluate_strings(
         self,
         *,
         prediction: str,

libs/langchain/langchain/evaluation/string_distance/base.py

@@ -10,6 +10,7 @@ from langchain_core.callbacks.manager import (
 )
 from langchain_core.utils import pre_init
 from pydantic import Field
+from typing_extensions import override
 
 from langchain.chains.base import Chain
 from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
@@ -260,6 +261,7 @@ class StringDistanceEvalChain(StringEvaluator, _RapidFuzzChainMixin):
         """
         return {"score": self.compute_metric(inputs["reference"], inputs["prediction"])}
 
+    @override
     def _evaluate_strings(
         self,
         *,
@@ -295,6 +297,7 @@ class StringDistanceEvalChain(StringEvaluator, _RapidFuzzChainMixin):
 
         return self._prepare_output(result)
 
+    @override
     async def _aevaluate_strings(
         self,
         *,

libs/langchain/langchain/hub.py

@@ -40,7 +40,7 @@ def _get_client(
 
 def push(
     repo_full_name: str,
-    object: Any,
+    object: Any,  # noqa: A002
     *,
     api_url: Optional[str] = None,
     api_key: Optional[str] = None,

libs/langchain/langchain/memory/vectorstore_token_buffer_memory.py

@@ -167,7 +167,7 @@ class ConversationVectorStoreTokenBufferMemory(ConversationTokenBufferMemory):
             self._pop_and_store_interaction(buffer)
 
     def _pop_and_store_interaction(self, buffer: list[BaseMessage]) -> None:
-        input = buffer.pop(0)
+        input_ = buffer.pop(0)
         output = buffer.pop(0)
         timestamp = self._timestamps.pop(0).strftime(TIMESTAMP_FORMAT)
         # Split AI output into smaller chunks to avoid creating documents
@@ -175,7 +175,7 @@ class ConversationVectorStoreTokenBufferMemory(ConversationTokenBufferMemory):
         ai_chunks = self._split_long_ai_text(str(output.content))
         for index, chunk in enumerate(ai_chunks):
             self.memory_retriever.save_context(
-                {"Human": f"<{timestamp}/00> {str(input.content)}"},
+                {"Human": f"<{timestamp}/00> {str(input_.content)}"},
                 {"AI": f"<{timestamp}/{index:02}> {chunk}"},
             )
 

libs/langchain/langchain/retrievers/ensemble.py

@@ -28,6 +28,7 @@ from langchain_core.runnables.utils import (
     get_unique_config_specs,
 )
 from pydantic import model_validator
+from typing_extensions import override
 
 T = TypeVar("T")
 H = TypeVar("H", bound=Hashable)
@@ -86,6 +87,7 @@ class EnsembleRetriever(BaseRetriever):
             values["weights"] = [1 / n_retrievers] * n_retrievers
         return values
 
+    @override
     def invoke(
         self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any
     ) -> list[Document]:
@@ -119,6 +121,7 @@ class EnsembleRetriever(BaseRetriever):
             )
             return result
 
+    @override
     async def ainvoke(
         self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any
     ) -> list[Document]:

libs/langchain/langchain/storage/file_system.py

@@ -88,23 +88,23 @@ class LocalFileStore(ByteStore):
 
         return Path(full_path)
 
-    def _mkdir_for_store(self, dir: Path) -> None:
+    def _mkdir_for_store(self, dir_path: Path) -> None:
         """Makes a store directory path (including parents) with specified permissions
 
         This is needed because `Path.mkdir()` is restricted by the current `umask`,
         whereas the explicit `os.chmod()` used here is not.
 
         Args:
-            dir: (Path) The store directory to make
+            dir_path: (Path) The store directory to make
 
         Returns:
             None
         """
-        if not dir.exists():
+        if not dir_path.exists():
-            self._mkdir_for_store(dir.parent)
+            self._mkdir_for_store(dir_path.parent)
-            dir.mkdir(exist_ok=True)
+            dir_path.mkdir(exist_ok=True)
             if self.chmod_dir is not None:
-                os.chmod(dir, self.chmod_dir)
+                os.chmod(dir_path, self.chmod_dir)
 
     def mget(self, keys: Sequence[str]) -> list[Optional[bytes]]:
         """Get the values associated with the given keys.

libs/langchain/pyproject.toml

@@ -143,7 +143,7 @@ ignore-regex = ".*(Stati Uniti|Tense=Pres).*"
 ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon,crate,aadd,symbl,precesses,accademia,nin"
 
 [tool.ruff.lint]
-select = ["E", "F", "I", "EM", "PGH003", "PIE", "T201", "D", "UP", "S", "W"]
+select = ["A", "E", "F", "I", "EM", "PGH003", "PIE", "T201", "D", "UP", "S", "W"]
 pydocstyle.convention = "google"
 pyupgrade.keep-runtime-typing = true
 

libs/langchain/tests/unit_tests/agents/output_parsers/test_convo_output_parser.py

@@ -33,10 +33,8 @@ print("Hello fifty shades of gray mans!"[::-1])  # noqa: T201
     )
 
 
-def _test_convo_output(
+def _test_convo_output(text: str, expected_tool: str, expected_tool_input: str) -> None:
-    input: str, expected_tool: str, expected_tool_input: str
+    result = ConvoOutputParser().parse(text.strip())
-) -> None:
-    result = ConvoOutputParser().parse(input.strip())
     assert isinstance(result, AgentAction)
     assert result.tool == expected_tool
     assert result.tool_input == expected_tool_input

libs/langchain/tests/unit_tests/chains/query_constructor/test_parser.py

@@ -25,7 +25,7 @@ def test_parse_invalid_grammar(x: str) -> None:
 def test_parse_comparison() -> None:
     comp = 'gte("foo", 2)'
     expected = Comparison(comparator=Comparator.GTE, attribute="foo", value=2)
-    for input in (
+    for text in (
         comp,
         comp.replace('"', "'"),
         comp.replace(" ", ""),
@@ -34,7 +34,7 @@ def test_parse_comparison() -> None:
         comp.replace(",", ", "),
         comp.replace("2", "2.0"),
     ):
-        actual = DEFAULT_PARSER.parse(input)
+        actual = DEFAULT_PARSER.parse(text)
         assert expected == actual
 
 
@@ -43,7 +43,7 @@ def test_parse_operation() -> None:
     eq = Comparison(comparator=Comparator.EQ, attribute="foo", value="bar")
     lt = Comparison(comparator=Comparator.LT, attribute="baz", value=1995.25)
     expected = Operation(operator=Operator.AND, arguments=[eq, lt])
-    for input in (
+    for text in (
         op,
         op.replace('"', "'"),
         op.replace(" ", ""),
@@ -52,7 +52,7 @@ def test_parse_operation() -> None:
         op.replace(",", ", "),
         op.replace("25", "250"),
     ):
-        actual = DEFAULT_PARSER.parse(input)
+        actual = DEFAULT_PARSER.parse(text)
         assert expected == actual
 
 

libs/langchain/tests/unit_tests/output_parsers/test_fix.py

@@ -152,7 +152,7 @@ def test_output_fixing_parser_output_type(
 
 
 @pytest.mark.parametrize(
-    "input,base_parser,retry_chain,expected",
+    "completion,base_parser,retry_chain,expected",
     [
         (
             "2024/07/08",
@@ -171,7 +171,7 @@ def test_output_fixing_parser_output_type(
     ],
 )
 def test_output_fixing_parser_parse_with_retry_chain(
-    input: str,
+    completion: str,
     base_parser: BaseOutputParser[T],
     retry_chain: Runnable[dict[str, Any], str],
     expected: T,
@@ -185,11 +185,11 @@ def test_output_fixing_parser_parse_with_retry_chain(
         retry_chain=retry_chain,
         legacy=False,
     )
-    assert parser.parse(input) == expected
+    assert parser.parse(completion) == expected
 
 
 @pytest.mark.parametrize(
-    "input,base_parser,retry_chain,expected",
+    "completion,base_parser,retry_chain,expected",
     [
         (
             "2024/07/08",
@@ -208,7 +208,7 @@ def test_output_fixing_parser_parse_with_retry_chain(
     ],
 )
 async def test_output_fixing_parser_aparse_with_retry_chain(
-    input: str,
+    completion: str,
     base_parser: BaseOutputParser[T],
     retry_chain: Runnable[dict[str, Any], str],
     expected: T,
@@ -221,7 +221,7 @@ async def test_output_fixing_parser_aparse_with_retry_chain(
         retry_chain=retry_chain,
         legacy=False,
     )
-    assert (await parser.aparse(input)) == expected
+    assert (await parser.aparse(completion)) == expected
 
 
 def _extract_exception(

libs/langchain/tests/unit_tests/output_parsers/test_retry.py

@@ -202,7 +202,7 @@ def test_retry_with_error_output_parser_parse_is_not_implemented() -> None:
 
 
 @pytest.mark.parametrize(
-    "input,prompt,base_parser,retry_chain,expected",
+    "completion,prompt,base_parser,retry_chain,expected",
     [
         (
             "2024/07/08",
@@ -215,7 +215,7 @@ def test_retry_with_error_output_parser_parse_is_not_implemented() -> None:
     ],
 )
 def test_retry_output_parser_parse_with_prompt_with_retry_chain(
-    input: str,
+    completion: str,
     prompt: PromptValue,
     base_parser: BaseOutputParser[T],
     retry_chain: Runnable[dict[str, Any], str],
@@ -226,11 +226,11 @@ def test_retry_output_parser_parse_with_prompt_with_retry_chain(
         retry_chain=retry_chain,
         legacy=False,
     )
-    assert parser.parse_with_prompt(input, prompt) == expected
+    assert parser.parse_with_prompt(completion, prompt) == expected
 
 
 @pytest.mark.parametrize(
-    "input,prompt,base_parser,retry_chain,expected",
+    "completion,prompt,base_parser,retry_chain,expected",
     [
         (
             "2024/07/08",
@@ -243,7 +243,7 @@ def test_retry_output_parser_parse_with_prompt_with_retry_chain(
     ],
 )
 async def test_retry_output_parser_aparse_with_prompt_with_retry_chain(
-    input: str,
+    completion: str,
     prompt: PromptValue,
     base_parser: BaseOutputParser[T],
     retry_chain: Runnable[dict[str, Any], str],
@@ -255,11 +255,11 @@ async def test_retry_output_parser_aparse_with_prompt_with_retry_chain(
         retry_chain=retry_chain,
         legacy=False,
     )
-    assert (await parser.aparse_with_prompt(input, prompt)) == expected
+    assert (await parser.aparse_with_prompt(completion, prompt)) == expected
 
 
 @pytest.mark.parametrize(
-    "input,prompt,base_parser,retry_chain,expected",
+    "completion,prompt,base_parser,retry_chain,expected",
     [
         (
             "2024/07/08",
@@ -272,7 +272,7 @@ async def test_retry_output_parser_aparse_with_prompt_with_retry_chain(
     ],
 )
 def test_retry_with_error_output_parser_parse_with_prompt_with_retry_chain(
-    input: str,
+    completion: str,
     prompt: PromptValue,
     base_parser: BaseOutputParser[T],
     retry_chain: Runnable[dict[str, Any], str],
@@ -284,11 +284,11 @@ def test_retry_with_error_output_parser_parse_with_prompt_with_retry_chain(
         retry_chain=retry_chain,
         legacy=False,
     )
-    assert parser.parse_with_prompt(input, prompt) == expected
+    assert parser.parse_with_prompt(completion, prompt) == expected
 
 
 @pytest.mark.parametrize(
-    "input,prompt,base_parser,retry_chain,expected",
+    "completion,prompt,base_parser,retry_chain,expected",
     [
         (
             "2024/07/08",
@@ -301,7 +301,7 @@ def test_retry_with_error_output_parser_parse_with_prompt_with_retry_chain(
     ],
 )
 async def test_retry_with_error_output_parser_aparse_with_prompt_with_retry_chain(
-    input: str,
+    completion: str,
     prompt: PromptValue,
     base_parser: BaseOutputParser[T],
     retry_chain: Runnable[dict[str, Any], str],
@@ -312,7 +312,7 @@ async def test_retry_with_error_output_parser_aparse_with_prompt_with_retry_chai
         retry_chain=retry_chain,
         legacy=False,
     )
-    assert (await parser.aparse_with_prompt(input, prompt)) == expected
+    assert (await parser.aparse_with_prompt(completion, prompt)) == expected
 
 
 def _extract_exception(

libs/langchain/tests/unit_tests/test_globals.py

@@ -56,16 +56,16 @@ def test_debug_is_settable_directly() -> None:
 def test_debug_is_settable_via_setter() -> None:
     from langchain_core.callbacks.manager import _get_debug
 
-    from langchain import globals
+    from langchain import globals as langchain_globals
 
-    previous_value = globals._debug
+    previous_value = langchain_globals._debug
     previous_fn_reading = _get_debug()
     assert previous_value == previous_fn_reading
 
     # Flip the value of the flag.
     set_debug(not previous_value)
 
-    new_value = globals._debug
+    new_value = langchain_globals._debug
     new_fn_reading = _get_debug()
 
     try:
@@ -115,17 +115,17 @@ def test_verbose_is_settable_directly() -> None:
 
 
 def test_verbose_is_settable_via_setter() -> None:
-    from langchain import globals
+    from langchain import globals as langchain_globals
     from langchain.chains.base import _get_verbosity
 
-    previous_value = globals._verbose
+    previous_value = langchain_globals._verbose
     previous_fn_reading = _get_verbosity()
     assert previous_value == previous_fn_reading
 
     # Flip the value of the flag.
     set_verbose(not previous_value)
 
-    new_value = globals._verbose
+    new_value = langchain_globals._verbose
     new_fn_reading = _get_verbosity()
 
     try:

libs/langchain/tests/unit_tests/test_imports.py

@@ -26,9 +26,9 @@ def test_import_all() -> None:
 
             mod = importlib.import_module(module_name)
 
-            all = getattr(mod, "__all__", [])
+            all_attrs = getattr(mod, "__all__", [])
 
-            for name in all:
+            for name in all_attrs:
                 # Attempt to import the name from the module
                 try:
                     obj = getattr(mod, name)
@@ -65,9 +65,9 @@ def test_import_all_using_dir() -> None:
         except ModuleNotFoundError as e:
             msg = f"Could not import {module_name}"
             raise ModuleNotFoundError(msg) from e
-        all = dir(mod)
+        attributes = dir(mod)
 
-        for name in all:
+        for name in attributes:
             if name.strip().startswith("_"):
                 continue
             # Attempt to import the name from the module

libs/langchain/uv.lock

@@ -2981,7 +2981,7 @@ requires-dist = [
 
 [package.metadata.requires-dev]
 codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }]
-lint = [{ name = "ruff", specifier = ">=0.9.2,<1.0.0" }]
+lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }]
 test = [{ name = "langchain-core", editable = "../core" }]
 test-integration = []
 typing = [
@@ -3007,7 +3007,7 @@ dev = [
 ]
 lint = [
     { name = "langchain-core", editable = "../core" },
-    { name = "ruff", specifier = ">=0.9.2,<1.0.0" },
+    { name = "ruff", specifier = ">=0.12.2,<0.13" },
 ]
 test = [
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },