core: Add ruff rules for pycodestyle Warning (W)

libs/core/langchain_core/documents/base.py

@@ -32,9 +32,9 @@ class BaseMedia(Serializable):
     id: Optional[str] = None
     """An optional identifier for the document.
 
-    Ideally this should be unique across the document collection and formatted 
+    Ideally this should be unique across the document collection and formatted
     as a UUID, but this will not be enforced.
-    
+
     .. versionadded:: 0.2.11
     """
 

libs/core/langchain_core/indexing/base.py

@@ -465,26 +465,26 @@ class DeleteResponse(TypedDict, total=False):
 
     num_deleted: int
     """The number of items that were successfully deleted.
-    
+
     If returned, this should only include *actual* deletions.
-    
-    If the ID did not exist to begin with, 
+
+    If the ID did not exist to begin with,
     it should not be included in this count.
     """
 
     succeeded: Sequence[str]
     """The IDs that were successfully deleted.
-    
+
     If returned, this should only include *actual* deletions.
-    
+
     If the ID did not exist to begin with,
     it should not be included in this list.
     """
 
     failed: Sequence[str]
     """The IDs that failed to be deleted.
-    
-    Please note that deleting an ID that 
+
+    Please note that deleting an ID that
     does not exist is **NOT** considered a failure.
     """
 

libs/core/langchain_core/language_models/base.py

@@ -100,12 +100,12 @@ class BaseLanguageModel(
 
     cache: Union[BaseCache, bool, None] = None
     """Whether to cache the response.
-    
+
     * If true, will use the global cache.
     * If false, will not use a cache
     * If None, will use the global cache if it's set, otherwise no cache.
     * If instance of BaseCache, will use the provided cache.
-    
+
     Caching is not currently supported for streaming methods of models.
     """
     verbose: bool = Field(default_factory=_get_verbosity, exclude=True, repr=False)

libs/core/langchain_core/language_models/chat_models.py

@@ -208,8 +208,8 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
 
     disable_streaming: Union[bool, Literal["tool_calling"]] = False
     """Whether to disable streaming for this model.
-    
-    If streaming is bypassed, then ``stream()/astream()`` will defer to 
+
+    If streaming is bypassed, then ``stream()/astream()`` will defer to
     ``invoke()/ainvoke()``.
 
     - If True, will always bypass streaming case.

libs/core/langchain_core/language_models/fake.py

@@ -21,12 +21,12 @@ class FakeListLLM(LLM):
     # it's only used by sub-classes.
     sleep: Optional[float] = None
     """Sleep time in seconds between responses.
-    
+
     Ignored by FakeListLLM, but used by sub-classes.
     """
     i: int = 0
     """Internally incremented after every model invocation.
-    
+
     Useful primarily for testing purposes.
     """
 

libs/core/langchain_core/messages/ai.py

@@ -65,7 +65,7 @@ class AIMessage(BaseMessage):
 
     example: bool = False
     """Use to denote that a message is part of an example conversation.
-    
+
     At the moment, this is ignored by most models. Usage is discouraged.
     """
 
@@ -215,7 +215,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["AIMessageChunk"] = "AIMessageChunk"  # type: ignore
-    """The type of the message (used for deserialization). 
+    """The type of the message (used for deserialization).
     Defaults to "AIMessageChunk"."""
 
     tool_call_chunks: list[ToolCallChunk] = []

libs/core/langchain_core/messages/base.py

@@ -25,7 +25,7 @@ class BaseMessage(Serializable):
 
     additional_kwargs: dict = Field(default_factory=dict)
     """Reserved for additional payload data associated with the message.
-    
+
     For example, for a message from an AI, this could include tool calls as
     encoded by the model provider.
     """
@@ -35,16 +35,16 @@ class BaseMessage(Serializable):
 
     type: str
     """The type of the message. Must be a string that is unique to the message type.
-    
+
     The purpose of this field is to allow for easy identification of the message type
     when deserializing messages.
     """
 
     name: Optional[str] = None
-    """An optional name for the message. 
-    
+    """An optional name for the message.
+
     This can be used to provide a human-readable name for the message.
-    
+
     Usage of this field is optional, and whether it's used or not is up to the
     model implementation.
     """

libs/core/langchain_core/messages/chat.py

@@ -35,7 +35,7 @@ class ChatMessageChunk(ChatMessage, BaseMessageChunk):
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["ChatMessageChunk"] = "ChatMessageChunk"  # type: ignore
-    """The type of the message (used during serialization). 
+    """The type of the message (used during serialization).
     Defaults to "ChatMessageChunk"."""
 
     @classmethod

libs/core/langchain_core/messages/function.py

@@ -42,7 +42,7 @@ class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["FunctionMessageChunk"] = "FunctionMessageChunk"  # type: ignore[assignment]
-    """The type of the message (used for serialization). 
+    """The type of the message (used for serialization).
     Defaults to "FunctionMessageChunk"."""
 
     @classmethod

libs/core/langchain_core/messages/human.py

@@ -30,7 +30,7 @@ class HumanMessage(BaseMessage):
 
     example: bool = False
     """Use to denote that a message is part of an example conversation.
-    
+
     At the moment, this is ignored by most models. Usage is discouraged.
     Defaults to False.
     """
@@ -66,7 +66,7 @@ class HumanMessageChunk(HumanMessage, BaseMessageChunk):
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["HumanMessageChunk"] = "HumanMessageChunk"  # type: ignore[assignment]
-    """The type of the message (used for serialization). 
+    """The type of the message (used for serialization).
     Defaults to "HumanMessageChunk"."""
 
     @classmethod

libs/core/langchain_core/messages/system.py

@@ -60,7 +60,7 @@ class SystemMessageChunk(SystemMessage, BaseMessageChunk):
     # to make sure that the chunk variant can be discriminated from the
     # non-chunk variant.
     type: Literal["SystemMessageChunk"] = "SystemMessageChunk"  # type: ignore[assignment]
-    """The type of the message (used for serialization). 
+    """The type of the message (used for serialization).
     Defaults to "SystemMessageChunk"."""
 
     @classmethod

libs/core/langchain_core/messages/tool.py

@@ -58,11 +58,11 @@ class ToolMessage(BaseMessage):
 
     artifact: Any = None
     """Artifact of the Tool execution which is not meant to be sent to the model.
-    
-    Should only be specified if it is different from the message content, e.g. if only 
+
+    Should only be specified if it is different from the message content, e.g. if only
     a subset of the full tool output is being passed as message content but the full
     output is needed in other parts of the code.
-    
+
     .. versionadded:: 0.2.17
     """
 
@@ -191,7 +191,7 @@ class ToolCall(TypedDict):
     """The arguments to the tool call."""
     id: Optional[str]
     """An identifier associated with the tool call.
-    
+
     An identifier is needed to associate a tool call request with a tool
     call result in events when multiple concurrent tool calls are made.
     """

libs/core/langchain_core/output_parsers/json.py

@@ -42,7 +42,7 @@ class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]):
     """
 
     pydantic_object: Annotated[Optional[type[TBaseModel]], SkipValidation()] = None  # type: ignore
-    """The Pydantic object to use for validation. 
+    """The Pydantic object to use for validation.
     If None, no validation is performed."""
 
     def _diff(self, prev: Optional[Any], next: Any) -> Any:

libs/core/langchain_core/output_parsers/openai_functions.py

@@ -57,9 +57,9 @@ class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
 
     strict: bool = False
     """Whether to allow non-JSON-compliant strings.
-    
+
     See: https://docs.python.org/3/library/json.html#encoders-and-decoders
-    
+
     Useful when the parsed output may include unicode characters or new lines.
     """
 
@@ -226,7 +226,7 @@ class Dog(BaseModel):
 
     pydantic_schema: Union[type[BaseModel], dict[str, type[BaseModel]]]
     """The pydantic schema to parse the output with.
-    
+
     If multiple schemas are provided, then the function name will be used to
     determine which schema to use.
     """

libs/core/langchain_core/output_parsers/openai_tools.py

@@ -142,12 +142,12 @@ class JsonOutputToolsParser(BaseCumulativeTransformOutputParser[Any]):
     first_tool_only: bool = False
     """Whether to return only the first tool call.
 
-    If False, the result will be a list of tool calls, or an empty list 
+    If False, the result will be a list of tool calls, or an empty list
     if no tool calls are found.
 
     If true, and multiple tool calls are found, only the first one will be returned,
-    and the other tool calls will be ignored. 
-    If no tool calls are found, None will be returned. 
+    and the other tool calls will be ignored.
+    If no tool calls are found, None will be returned.
     """
 
     def parse_result(self, result: list[Generation], *, partial: bool = False) -> Any:

libs/core/langchain_core/output_parsers/xml.py

@@ -12,12 +12,12 @@
 from langchain_core.runnables.utils import AddableDict
 
 XML_FORMAT_INSTRUCTIONS = """The output should be formatted as a XML file.
-1. Output should conform to the tags below. 
+1. Output should conform to the tags below.
 2. If tags are not given, make them on your own.
 3. Remember to always open and close all the tags.
 
 As an example, for the tags ["foo", "bar", "baz"]:
-1. String "<foo>\n   <bar>\n      <baz></baz>\n   </bar>\n</foo>" is a well-formatted instance of the schema. 
+1. String "<foo>\n   <bar>\n      <baz></baz>\n   </bar>\n</foo>" is a well-formatted instance of the schema.
 2. String "<foo>\n   <bar>\n   </foo>" is a badly-formatted instance.
 3. String "<foo>\n   <tag>\n   </tag>\n</foo>" is a badly-formatted instance.
 
@@ -146,23 +146,23 @@ class XMLOutputParser(BaseTransformOutputParser):
     )
     parser: Literal["defusedxml", "xml"] = "defusedxml"
     """Parser to use for XML parsing. Can be either 'defusedxml' or 'xml'.
-    
-    * 'defusedxml' is the default parser and is used to prevent XML vulnerabilities 
+
+    * 'defusedxml' is the default parser and is used to prevent XML vulnerabilities
        present in some distributions of Python's standard library xml.
        `defusedxml` is a wrapper around the standard library parser that
        sets up the parser with secure defaults.
     * 'xml' is the standard library parser.
-    
+
     Use `xml` only if you are sure that your distribution of the standard library
-    is not vulnerable to XML vulnerabilities. 
-    
+    is not vulnerable to XML vulnerabilities.
+
     Please review the following resources for more information:
-    
+
     * https://docs.python.org/3/library/xml.html#xml-vulnerabilities
-    * https://github.com/tiran/defusedxml 
-    
+    * https://github.com/tiran/defusedxml
+
     The standard library relies on libexpat for parsing XML:
-    https://github.com/libexpat/libexpat 
+    https://github.com/libexpat/libexpat
     """
 
     def get_format_instructions(self) -> str:

libs/core/langchain_core/outputs/chat_result.py

@@ -20,16 +20,16 @@ class ChatResult(BaseModel):
 
     generations: list[ChatGeneration]
     """List of the chat generations.
-    
+
     Generations is a list to allow for multiple candidate generations for a single
     input prompt.
     """
     llm_output: Optional[dict] = None
     """For arbitrary LLM provider specific output.
-    
+
     This dictionary is a free-form dictionary that can contain any information that the
     provider wants to return. It is not standardized and is provider-specific.
-    
+
     Users should generally avoid relying on this field and instead rely on
     accessing relevant information from standardized fields present in
     AIMessage.

libs/core/langchain_core/outputs/generation.py

@@ -26,8 +26,8 @@ class Generation(Serializable):
     """Generated text output."""
 
     generation_info: Optional[dict[str, Any]] = None
-    """Raw response from the provider. 
-    
+    """Raw response from the provider.
+
     May include things like the reason for finishing or token log probabilities.
     """
     type: Literal["Generation"] = "Generation"

libs/core/langchain_core/outputs/llm_result.py

@@ -22,25 +22,25 @@ class LLMResult(BaseModel):
         list[Union[Generation, ChatGeneration, GenerationChunk, ChatGenerationChunk]]
     ]
     """Generated outputs.
-    
+
     The first dimension of the list represents completions for different input
     prompts.
-    
+
     The second dimension of the list represents different candidate generations
     for a given prompt.
-    
+
     When returned from an LLM the type is List[List[Generation]].
     When returned from a chat model the type is List[List[ChatGeneration]].
-    
+
     ChatGeneration is a subclass of Generation that has a field for a structured
     chat message.
     """
     llm_output: Optional[dict] = None
     """For arbitrary LLM provider specific output.
-    
+
     This dictionary is a free-form dictionary that can contain any information that the
     provider wants to return. It is not standardized and is provider-specific.
-    
+
     Users should generally avoid relying on this field and instead rely on
     accessing relevant information from standardized fields present in
     AIMessage.

libs/core/langchain_core/prompts/base.py

@@ -45,11 +45,11 @@ class BasePromptTemplate(
     """Base class for all prompt templates, returning a prompt."""
 
     input_variables: list[str]
-    """A list of the names of the variables whose values are required as inputs to the 
+    """A list of the names of the variables whose values are required as inputs to the
     prompt."""
     optional_variables: list[str] = Field(default=[])
     """optional_variables: A list of the names of the variables for placeholder
-       or MessagePlaceholder that are optional. These variables are auto inferred 
+       or MessagePlaceholder that are optional. These variables are auto inferred
        from the prompt and user need not provide them."""
     input_types: typing.Dict[str, Any] = Field(default_factory=dict, exclude=True)  # noqa: UP006
     """A dictionary of the types of the variables the prompt template expects.
@@ -58,7 +58,7 @@ class BasePromptTemplate(
     """How to parse the output of calling an LLM on this formatted prompt."""
     partial_variables: Mapping[str, Any] = Field(default_factory=dict)
     """A dictionary of the partial variables the prompt template carries.
-    
+
     Partial variables populate the template so that you don't need to
     pass them in every time you call the prompt."""
     metadata: Optional[typing.Dict[str, Any]] = None  # noqa: UP006

libs/core/langchain_core/prompts/chat.py

@@ -196,12 +196,12 @@ class MessagesPlaceholder(BaseMessagePromptTemplate):
     """Name of variable to use as messages."""
 
     optional: bool = False
-    """If True format_messages can be called with no arguments and will return an empty 
-        list. If False then a named argument with name `variable_name` must be passed 
+    """If True format_messages can be called with no arguments and will return an empty
+        list. If False then a named argument with name `variable_name` must be passed
         in, even if the value is an empty list."""
 
     n_messages: Optional[PositiveInt] = None
-    """Maximum number of messages to include. If None, then will include all. 
+    """Maximum number of messages to include. If None, then will include all.
     Defaults to None."""
 
     @classmethod