fix: formatting issues in docstrings (#32265)

Ensures proper reStructuredText formatting by adding the required blank
line before closing docstring quotes, which resolves the "Block quote
ends without a blank line; unexpected unindent" warning.

libs/cli/langchain_cli/integration_template/integration_template/document_loaders.py

@@ -61,6 +61,7 @@ class __ModuleName__Loader(BaseLoader):
         .. code-block:: python
 
             TODO: Example output
+
     """  # noqa: E501
 
     # TODO: This method must be implemented to load documents.

libs/cli/langchain_cli/integration_template/integration_template/tools.py

@@ -61,6 +61,7 @@ class __ModuleName__Tool(BaseTool):  # type: ignore[override]
         .. code-block:: python
 
             # TODO: output of invocation
+
     """  # noqa: E501
 
     # TODO: Set tool name and description

libs/core/langchain_core/_api/beta_decorator.py

@@ -70,6 +70,7 @@ def beta(
             @beta
             def the_function_to_annotate():
                 pass
+
     """
 
     def beta(

libs/core/langchain_core/_api/deprecation.py

@@ -136,6 +136,7 @@ def deprecated(
             @deprecated('1.4.0')
             def the_function_to_deprecate():
                 pass
+
     """
     _validate_deprecation_params(
         removal, alternative, alternative_import, pending=pending
@@ -549,6 +550,7 @@ def rename_parameter(
 
             @_api.rename_parameter("3.1", "bad_name", "good_name")
             def func(good_name): ...
+
     """
 
     def decorator(f: Callable[_P, _R]) -> Callable[_P, _R]:

libs/core/langchain_core/beta/runnables/context.py

@@ -363,6 +363,7 @@ class Context:
             print(output["result"])  # Output: "hello"
             print(output["context"])  # Output: "What's your name?"
             print(output["input"])  # Output: "What's your name?
+
     """
 
     @staticmethod

libs/core/langchain_core/callbacks/file.py

@@ -53,6 +53,7 @@ class FileCallbackHandler(BaseCallbackHandler):
         When not used as a context manager, a deprecation warning will be issued
         on first use. The file will be opened immediately in ``__init__`` and closed
         in ``__del__`` or when ``close()`` is called explicitly.
+
     """
 
     def __init__(

libs/core/langchain_core/callbacks/manager.py

@@ -105,6 +105,7 @@ def trace_as_chain_group(
                 # Use the callback manager for the chain group
                 res = llm.invoke(llm_input, {"callbacks": manager})
                 manager.on_chain_end({"output": res})
+
     """  # noqa: E501
     from langchain_core.tracers.context import _get_trace_callbacks
 
@@ -186,6 +187,7 @@ async def atrace_as_chain_group(
                 # Use the async callback manager for the chain group
                 res = await llm.ainvoke(llm_input, {"callbacks": manager})
                 await manager.on_chain_end({"output": res})
+
     """  # noqa: E501
     from langchain_core.tracers.context import _get_trace_callbacks
 
@@ -2575,6 +2577,7 @@ async def adispatch_custom_event(
         behalf.
 
     .. versionadded:: 0.2.15
+
     """
     from langchain_core.runnables.config import (
         ensure_config,
@@ -2645,6 +2648,7 @@ def dispatch_custom_event(
             foo_.invoke({"a": "1"}, {"callbacks": [CustomCallbackManager()]})
 
     .. versionadded:: 0.2.15
+
     """
     from langchain_core.runnables.config import (
         ensure_config,

libs/core/langchain_core/callbacks/usage.py

@@ -44,6 +44,7 @@ class UsageMetadataCallbackHandler(BaseCallbackHandler):
               'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}
 
     .. versionadded:: 0.3.49
+
     """
 
     def __init__(self) -> None:
@@ -127,6 +128,7 @@ def get_usage_metadata_callback(
               'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}
 
     .. versionadded:: 0.3.49
+
     """
     from langchain_core.tracers.context import register_configure_hook
 

libs/core/langchain_core/chat_history.py

@@ -91,6 +91,7 @@ class BaseChatMessageHistory(ABC):
                 def clear(self):
                     with open(os.path.join(storage_path, session_id), "w") as f:
                         f.write("[]")
+
     """
 
     messages: list[BaseMessage]

libs/core/langchain_core/document_loaders/langsmith.py

@@ -36,6 +36,7 @@ class LangSmithLoader(BaseLoader):
             # -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]
 
     .. versionadded:: 0.2.34
+
     """  # noqa: E501
 
     def __init__(

libs/core/langchain_core/documents/base.py

@@ -102,6 +102,7 @@ class Blob(BaseMedia):
             # Read the blob as a byte stream
             with blob.as_bytes_io() as f:
                 print(f.read())
+
     """
 
     data: Union[bytes, str, None] = None
@@ -265,6 +266,7 @@ class Document(BaseMedia):
                 page_content="Hello, world!",
                 metadata={"source": "https://example.com"}
             )
+
     """
 
     page_content: str

libs/core/langchain_core/embeddings/fake.py

@@ -46,6 +46,7 @@ class FakeEmbeddings(Embeddings, BaseModel):
 
             2
             [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257]
+
     """
 
     size: int
@@ -103,6 +104,7 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
 
             2
             [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257]
+
     """
 
     size: int

libs/core/langchain_core/language_models/_utils.py

@@ -51,6 +51,7 @@ def _parse_data_uri(uri: str) -> Optional[dict]:
                 "mime_type": "image/jpeg",
                 "data": "/9j/4AAQSkZJRg...",
             }
+
     """
     regex = r"^data:(?P<mime_type>[^;]+);base64,(?P<data>.+)$"
     match = re.match(regex, uri)

libs/core/langchain_core/language_models/chat_models.py

@@ -1467,6 +1467,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         .. versionchanged:: 0.2.26
 
                 Added support for TypedDict class.
+
         """  # noqa: E501
         _ = kwargs.pop("method", None)
         _ = kwargs.pop("strict", None)

libs/core/langchain_core/language_models/llms.py

@@ -1418,6 +1418,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             .. code-block:: python
 
                 llm.save(file_path="path/llm.yaml")
+
         """
         # Convert file to Path object.
         save_path = Path(file_path)

libs/core/langchain_core/memory.py

@@ -53,6 +53,7 @@ class BaseMemory(Serializable, ABC):
 
                 def clear(self) -> None:
                     pass
+
     """  # noqa: E501
 
     model_config = ConfigDict(

libs/core/langchain_core/messages/ai.py

@@ -57,6 +57,7 @@ class InputTokenDetails(TypedDict, total=False):
     .. versionadded:: 0.3.9
 
     May also hold extra provider-specific keys.
+
     """
 
     audio: int
@@ -89,6 +90,7 @@ class OutputTokenDetails(TypedDict, total=False):
             }
 
     .. versionadded:: 0.3.9
+
     """
 
     audio: int
@@ -128,6 +130,7 @@ class UsageMetadata(TypedDict):
     .. versionchanged:: 0.3.9
 
         Added ``input_token_details`` and ``output_token_details``.
+
     """
 
     input_tokens: int

libs/core/langchain_core/messages/human.py

@@ -28,6 +28,7 @@ class HumanMessage(BaseMessage):
             # Instantiate a chat model and invoke it with the messages
             model = ...
             print(model.invoke(messages))
+
     """
 
     example: bool = False

libs/core/langchain_core/messages/tool.py

@@ -59,6 +59,7 @@ class ToolMessage(BaseMessage, ToolOutputMixin):
     The tool_call_id field is used to associate the tool call request with the
     tool call response. This is useful in situations where a chat model is able
     to request multiple tool calls in parallel.
+
     """  # noqa: E501
 
     tool_call_id: str
@@ -191,6 +192,7 @@ class ToolCall(TypedDict):
 
         This represents a request to call the tool named "foo" with arguments {"a": 1}
         and an identifier of "123".
+
     """
 
     name: str
@@ -240,6 +242,7 @@ class ToolCallChunk(TypedDict):
             AIMessageChunk(content="", tool_call_chunks=left_chunks)
             + AIMessageChunk(content="", tool_call_chunks=right_chunks)
         ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)]
+
     """
 
     name: Optional[str]

libs/core/langchain_core/messages/utils.py

@@ -111,6 +111,7 @@ def get_buffer_string(
             ]
             get_buffer_string(messages)
             # -> "Human: Hi, how are you?\nAI: Good, how are you?"
+
     """
     string_messages = []
     for m in messages:
@@ -463,6 +464,7 @@ def filter_messages(
                 SystemMessage("you're a good assistant."),
                 HumanMessage("what's your name", id="foo", name="example_user"),
             ]
+
     """  # noqa: E501
     messages = convert_to_messages(messages)
     filtered: list[BaseMessage] = []
@@ -869,6 +871,7 @@ def trim_messages(
                     HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="first"),
                     AIMessage( [{"type": "text", "text": "This is the FIRST 4 token block."}], id="second"),
                 ]
+
     """  # noqa: E501
     # Validate arguments
     if start_on and strategy == "first":

libs/core/langchain_core/output_parsers/base.py

@@ -155,6 +155,7 @@ class BaseOutputParser(
                 @property
                 def _type(self) -> str:
                     return "boolean_output_parser"
+
     """  # noqa: E501
 
     @property

libs/core/langchain_core/output_parsers/openai_functions.py

@@ -214,6 +214,7 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
                 pydantic_schema={"cookie": Cookie, "dog": Dog}
             )
             result = parser.parse_result([chat_generation])
+
     """
 
     pydantic_schema: Union[type[BaseModel], dict[str, type[BaseModel]]]

libs/core/langchain_core/prompts/base.py

@@ -307,6 +307,7 @@ class BasePromptTemplate(
         .. code-block:: python
 
             prompt.format(variable1="foo")
+
         """
 
     async def aformat(self, **kwargs: Any) -> FormatOutputType:
@@ -323,6 +324,7 @@ class BasePromptTemplate(
         .. code-block:: python
 
             await prompt.aformat(variable1="foo")
+
         """
         return self.format(**kwargs)
 
@@ -363,6 +365,7 @@ class BasePromptTemplate(
         .. code-block:: python
 
             prompt.save(file_path="path/prompt.yaml")
+
         """
         if self.partial_variables:
             msg = "Cannot save prompt with partial variables."
@@ -442,6 +445,7 @@ def format_document(doc: Document, prompt: BasePromptTemplate[str]) -> str:
             prompt = PromptTemplate.from_template("Page {page}: {page_content}")
             format_document(doc, prompt)
             >>> "Page 1: This is a joke"
+
     """
     return prompt.format(**_get_document_info(doc, prompt))
 

libs/core/langchain_core/prompts/chat.py

@@ -126,6 +126,7 @@ class MessagesPlaceholder(BaseMessagePromptTemplate):
             # -> [
             #     HumanMessage(content="Hello!"),
             # ]
+
     """
 
     variable_name: str
@@ -1164,6 +1165,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
 
         Returns:
             a chat prompt template.
+
         """
         return cls(messages, template_format=template_format)
 
@@ -1248,6 +1250,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
                 template2 = template.partial(user="Lucy", name="R2D2")
 
                 template2.format_messages(input="hello")
+
         """
         prompt_dict = self.__dict__.copy()
         prompt_dict["input_variables"] = list(

libs/core/langchain_core/prompts/few_shot.py

@@ -357,6 +357,7 @@ class FewShotChatMessagePromptTemplate(
             from langchain_core.chat_models import ChatAnthropic
             chain = final_prompt | ChatAnthropic(model="claude-3-haiku-20240307")
             chain.invoke({"input": "What's 3+3?"})
+
     """
 
     input_variables: list[str] = Field(default_factory=list)

libs/core/langchain_core/prompts/few_shot_with_templates.py

@@ -122,6 +122,7 @@ class FewShotPromptWithTemplates(StringPromptTemplate):
         .. code-block:: python
 
             prompt.format(variable1="foo")
+
         """
         kwargs = self._merge_partial_and_user_variables(**kwargs)
         # Get the examples to use.

libs/core/langchain_core/prompts/image.py

@@ -90,6 +90,7 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
             .. code-block:: python
 
                 prompt.format(variable1="foo")
+
         """
         formatted = {}
         for k, v in self.template.items():

libs/core/langchain_core/prompts/pipeline.py

@@ -45,6 +45,7 @@ class PipelinePromptTemplate(BasePromptTemplate):
           Each PromptTemplate will be formatted and then passed
           to future prompt templates as a variable with
           the same name as `name`
+
     """
 
     final_prompt: BasePromptTemplate

libs/core/langchain_core/prompts/prompt.py

@@ -56,6 +56,7 @@ class PromptTemplate(StringPromptTemplate):
 
             # Instantiation using initializer
             prompt = PromptTemplate(template="Say {foo}")
+
     """
 
     @property

libs/core/langchain_core/prompts/structured.py

@@ -115,6 +115,7 @@ class StructuredPrompt(ChatPromptTemplate):
 
         Returns:
             a structured prompt template
+
         """
         return cls(messages, schema, **kwargs)
 

libs/core/langchain_core/rate_limiters.py

@@ -123,6 +123,7 @@ class InMemoryRateLimiter(BaseRateLimiter):
 
 
     .. versionadded:: 0.2.24
+
     """  # noqa: E501
 
     def __init__(

libs/core/langchain_core/retrievers.py

@@ -124,6 +124,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
                     # Op -- (n_docs,1) -- Cosine Sim with each doc
                     results = cosine_similarity(self.tfidf_array, query_vec).reshape((-1,))
                     return [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
+
     """  # noqa: E501
 
     model_config = ConfigDict(
@@ -230,6 +231,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
         .. code-block:: python
 
             retriever.invoke("query")
+
         """
         from langchain_core.callbacks.manager import CallbackManager
 
@@ -294,6 +296,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
         .. code-block:: python
 
             await retriever.ainvoke("query")
+
         """
         from langchain_core.callbacks.manager import AsyncCallbackManager
 

libs/core/langchain_core/runnables/base.py

@@ -236,6 +236,7 @@ class Runnable(ABC, Generic[Input, Output]):
             )
 
     For a UI (and much more) checkout LangSmith: https://docs.smith.langchain.com/
+
     """  # noqa: E501
 
     name: Optional[str]
@@ -391,6 +392,7 @@ class Runnable(ABC, Generic[Input, Output]):
                 print(runnable.get_input_jsonschema())
 
         .. versionadded:: 0.3.0
+
         """
         return self.get_input_schema(config).model_json_schema()
 
@@ -464,6 +466,7 @@ class Runnable(ABC, Generic[Input, Output]):
                 print(runnable.get_output_jsonschema())
 
         .. versionadded:: 0.3.0
+
         """
         return self.get_output_schema(config).model_json_schema()
 
@@ -620,6 +623,7 @@ class Runnable(ABC, Generic[Input, Output]):
                 sequence.batch([1, 2, 3])
                 await sequence.abatch([1, 2, 3])
                 # -> [4, 6, 8]
+
         """
         return RunnableSequence(self, *others, name=name)
 
@@ -1361,6 +1365,7 @@ class Runnable(ABC, Generic[Input, Output]):
 
         Raises:
             NotImplementedError: If the version is not `v1` or `v2`.
+
         """  # noqa: E501
         from langchain_core.tracers.event_stream import (
             _astream_events_implementation_v1,
@@ -1607,6 +1612,7 @@ class Runnable(ABC, Generic[Input, Output]):
                 on_end=fn_end
             )
             chain.invoke(2)
+
         """
         from langchain_core.tracers.root_listeners import RootListenersTracer
 
@@ -1825,6 +1831,7 @@ class Runnable(ABC, Generic[Input, Output]):
 
                     runnable = RunnableLambda(_lambda)
                     print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]
+
         """
         return RunnableEach(bound=self)
 
@@ -2446,6 +2453,7 @@ class Runnable(ABC, Generic[Input, Output]):
             as_tool.invoke("b")
 
         .. versionadded:: 0.2.14
+
         """
         # Avoid circular import
         from langchain_core.tools import convert_runnable_to_tool
@@ -2517,6 +2525,7 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]):
                 configurable={"output_token_number": 200}
                 ).invoke("tell me something about chess").content
             )
+
         """
         from langchain_core.runnables.configurable import RunnableConfigurableFields
 
@@ -2577,6 +2586,7 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]):
                     configurable={"llm": "openai"}
                 ).invoke("which organization created you?").content
             )
+
         """
         from langchain_core.runnables.configurable import (
             RunnableConfigurableAlternatives,
@@ -2741,6 +2751,7 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
             async for chunk in chain.astream({'topic': 'colors'}):
                 print('-')  # noqa: T201
                 print(chunk, sep='', flush=True)  # noqa: T201
+
     """
 
     # The steps are broken into first, middle and last, solely for type checking
@@ -3539,6 +3550,7 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
                 for key in chunk:
                     output[key] = output[key] + chunk[key].content
                 print(output)  # noqa: T201
+
     """
 
     steps__: Mapping[str, Runnable[Input, Any]]
@@ -4061,6 +4073,7 @@ class RunnableGenerator(Runnable[Input, Output]):
 
             runnable = chant_chain | RunnableLambda(reverse_generator)
             "".join(runnable.stream({"topic": "waste"}))  # ".elcycer ,esuer ,ecudeR"
+
     """
 
     def __init__(
@@ -4321,6 +4334,7 @@ class RunnableLambda(Runnable[Input, Output]):
             runnable = RunnableLambda(add_one, afunc=add_one_async)
             runnable.invoke(1) # Uses add_one
             await runnable.ainvoke(1) # Uses add_one_async
+
     """
 
     def __init__(
@@ -5175,6 +5189,7 @@ class RunnableEach(RunnableEachBase[Input, Output]):
                                         {'topic':'Art'},
                                         {'topic':'Biology'}])
             print(output)  # noqa: T201
+
     """
 
     @override
@@ -5709,6 +5724,7 @@ class RunnableBinding(RunnableBindingBase[Input, Output]):
                 kwargs={'stop': ['-']} # <-- Note the additional kwargs
             )
             runnable_binding.invoke('Say "Parrot-MAGIC"') # Should return `Parrot`
+
     """
 
     @override
@@ -5989,5 +6005,6 @@ def chain(
 
             for chunk in llm.stream(formatted):
                 yield chunk
+
     """
     return RunnableLambda(func)

libs/core/langchain_core/runnables/branch.py

@@ -63,6 +63,7 @@ class RunnableBranch(RunnableSerializable[Input, Output]):
 
             branch.invoke("hello") # "HELLO"
             branch.invoke(None) # "goodbye"
+
     """
 
     branches: Sequence[tuple[Runnable[Input, bool], Runnable[Input, Output]]]

libs/core/langchain_core/runnables/configurable.py

@@ -378,6 +378,7 @@ class RunnableConfigurableFields(DynamicRunnable[Input, Output]):
                 {"question": "foo", "context": "bar"},
                 config={"configurable": {"hub_commit": "rlm/rag-prompt-llama"}},
             )
+
     """
 
     fields: dict[str, AnyConfigurableField]

libs/core/langchain_core/runnables/fallbacks.py

@@ -85,6 +85,7 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]):
                 | model
                 | StrOutputParser()
             ).with_fallbacks([RunnableLambda(when_all_is_lost)])
+
     """
 
     runnable: Runnable[Input, Output]

libs/core/langchain_core/runnables/graph.py

@@ -611,6 +611,7 @@ class Graph:
 
         Returns:
             The Mermaid syntax string.
+
         """
         from langchain_core.runnables.graph_mermaid import draw_mermaid
 
@@ -681,6 +682,7 @@ class Graph:
 
         Returns:
             The PNG image as bytes.
+
         """
         from langchain_core.runnables.graph_mermaid import draw_mermaid_png
 

libs/core/langchain_core/runnables/graph_ascii.py

@@ -263,6 +263,7 @@ def draw_ascii(vertices: Mapping[str, str], edges: Sequence[LangEdge]) -> str:
             +---+     +---+
             | 3 |     | 4 |
             +---+     +---+
+
     """
     # NOTE: coordinates might me negative, so we need to shift
     # everything to the positive plane before we actually draw it.

libs/core/langchain_core/runnables/graph_mermaid.py

@@ -70,6 +70,7 @@ def draw_mermaid(
 
     Returns:
         str: Mermaid graph syntax.
+
     """
     # Initialize Mermaid graph configuration
     original_frontmatter_config = frontmatter_config or {}

libs/core/langchain_core/runnables/history.py

@@ -311,6 +311,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):
                 into the get_session_history factory.
             **kwargs: Arbitrary additional kwargs to pass to parent class
                 ``RunnableBindingBase`` init.
+
         """
         history_chain: Runnable = RunnableLambda(
             self._enter_history, self._aenter_history

libs/core/langchain_core/runnables/passthrough.py

@@ -132,6 +132,7 @@ class RunnablePassthrough(RunnableSerializable[Other, Other]):
 
             runnable.invoke('hello')
             # {'llm1': 'completion', 'llm2': 'completion', 'total_chars': 20}
+
     """
 
     input_type: Optional[type[Other]] = None
@@ -393,6 +394,7 @@ class RunnableAssign(RunnableSerializable[dict[str, Any], dict[str, Any]]):
             # Asynchronous example
             await runnable_assign.ainvoke({"input": 5})
             # returns {'input': 5, 'add_step': {'added': 15}}
+
     """
 
     mapper: RunnableParallel
@@ -697,6 +699,7 @@ class RunnablePick(RunnableSerializable[dict[str, Any], dict[str, Any]]):
             output_data = runnable.invoke(input_data)
 
             print(output_data)  # Output: {'name': 'John', 'age': 30}
+
     """
 
     keys: Union[str, list[str]]

libs/core/langchain_core/runnables/retry.py

@@ -110,6 +110,7 @@ class RunnableRetry(RunnableBindingBase[Input, Output]):
             # Bad
             chain = template | model
             retryable_chain = chain.with_retry()
+
     """  # noqa: E501
 
     retry_exception_types: tuple[type[BaseException], ...] = (Exception,)

libs/core/langchain_core/runnables/router.py

@@ -66,6 +66,7 @@ class RouterRunnable(RunnableSerializable[RouterInput, Output]):
 
             router = RouterRunnable(runnables={"add": add, "square": square})
             router.invoke({"key": "square", "input": 3})
+
     """
 
     runnables: Mapping[str, Runnable[Any, Output]]

libs/core/langchain_core/runnables/schema.py

@@ -83,6 +83,7 @@ class BaseStreamEvent(TypedDict):
                     "tags": [],
                 },
             ]
+
     """
 
     event: str

libs/core/langchain_core/stores.py

@@ -76,6 +76,7 @@ class BaseStore(ABC, Generic[K, V]):
                         for key in self.store.keys():
                             if key.startswith(prefix):
                                 yield key
+
     """
 
     @abstractmethod
@@ -302,6 +303,7 @@ class InMemoryStore(InMemoryBaseStore[Any]):
             # ['key2']
             list(store.yield_keys(prefix='k'))
             # ['key2']
+
     """
 
 
@@ -327,6 +329,7 @@ class InMemoryByteStore(InMemoryBaseStore[bytes]):
             # ['key2']
             list(store.yield_keys(prefix='k'))
             # ['key2']
+
     """
 
 

libs/core/langchain_core/tools/base.py

@@ -1273,6 +1273,7 @@ class InjectedToolCallId(InjectedToolArg):
                 name="foo",
                 tool_call_id=tool_call_id
             )
+
     """
 
 

libs/core/langchain_core/tools/convert.py

@@ -215,6 +215,7 @@ def tool(
                     monkey: The baz.
                 \"\"\"
                 return bar
+
     """  # noqa: D214, D410, D411
 
     def _create_tool_factory(

libs/core/langchain_core/tools/structured.py

@@ -174,6 +174,7 @@ class StructuredTool(BaseTool):
                     return a + b
                 tool = StructuredTool.from_function(add)
                 tool.run(1, 2) # 3
+
         """
         if func is not None:
             source_function = func

libs/core/langchain_core/utils/aiter.py

@@ -189,6 +189,7 @@ class Tee(Generic[T]):
     To enforce sequential use of ``anext``, provide a ``lock``
     - e.g. an :py:class:`asyncio.Lock` instance in an :py:mod:`asyncio` application -
     and access is automatically synchronised.
+
     """
 
     def __init__(
@@ -280,6 +281,7 @@ class aclosing(AbstractAsyncContextManager):  # noqa: N801
             <block>
         finally:
             await agen.aclose()
+
     """
 
     def __init__(

libs/core/langchain_core/utils/function_calling.py

@@ -687,6 +687,7 @@ def tool_example_to_messages(
                 messages.extend(
                     tool_example_to_messages(txt, [tool_call])
                 )
+
     """
     messages: list[BaseMessage] = [HumanMessage(content=input)]
     openai_tool_calls = [

libs/core/langchain_core/utils/iter.py

@@ -126,6 +126,7 @@ class Tee(Generic[T]):
     To enforce sequential use of ``anext``, provide a ``lock``
     - e.g. an :py:class:`asyncio.Lock` instance in an :py:mod:`asyncio` application -
     and access is automatically synchronised.
+
     """
 
     def __init__(

libs/core/langchain_core/vectorstores/base.py

@@ -994,6 +994,7 @@ class VectorStore(ABC):
             docsearch.as_retriever(
                 search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
             )
+
         """
         tags = kwargs.pop("tags", None) or [*self._get_retriever_tags()]
         return VectorStoreRetriever(vectorstore=self, tags=tags, **kwargs)

libs/core/tests/unit_tests/conftest.py

@@ -66,6 +66,7 @@ def pytest_collection_modifyitems(
         @pytest.mark.requires("package1", "package2")
         def test_something():
             ...
+
     """
     # Mapping from the name of a package to whether it is installed or not.
     # Used to avoid repeated calls to `util.find_spec`

libs/langchain/langchain/agents/agent.py

@@ -196,6 +196,7 @@ class BaseSingleActionAgent(BaseModel):
 
             # If working with agent executor
             agent.agent.save(file_path="path/agent.yaml")
+
         """
         # Convert file to Path object.
         save_path = Path(file_path) if isinstance(file_path, str) else file_path
@@ -339,6 +340,7 @@ class BaseMultiActionAgent(BaseModel):
 
             # If working with agent executor
             agent.agent.save(file_path="path/agent.yaml")
+
         """
         # Convert file to Path object.
         save_path = Path(file_path) if isinstance(file_path, str) else file_path

libs/langchain/langchain/agents/agent_toolkits/vectorstore/base.py

@@ -90,6 +90,7 @@ def create_vectorstore_agent(
 
     Returns:
         AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response
+
     """  # noqa: E501
     tools = toolkit.get_tools()
     prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
@@ -198,6 +199,7 @@ def create_vectorstore_router_agent(
 
     Returns:
         AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response.
+
     """  # noqa: E501
     tools = toolkit.get_tools()
     prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)

libs/langchain/langchain/agents/json_chat/base.py

@@ -160,6 +160,7 @@ def create_json_chat_agent(
                     MessagesPlaceholder("agent_scratchpad"),
                 ]
             )
+
     """  # noqa: E501
     missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
         prompt.input_variables + list(prompt.partial_variables),

libs/langchain/langchain/agents/openai_functions_agent/base.py

@@ -359,6 +359,7 @@ def create_openai_functions_agent(
                     MessagesPlaceholder("agent_scratchpad"),
                 ]
             )
+
     """
     if "agent_scratchpad" not in (
         prompt.input_variables + list(prompt.partial_variables)

libs/langchain/langchain/agents/openai_tools/base.py

@@ -84,6 +84,7 @@ def create_openai_tools_agent(
                     MessagesPlaceholder("agent_scratchpad"),
                 ]
             )
+
     """
     missing_vars = {"agent_scratchpad"}.difference(
         prompt.input_variables + list(prompt.partial_variables),

libs/langchain/langchain/agents/react/agent.py

@@ -116,6 +116,7 @@ def create_react_agent(
             Thought:{agent_scratchpad}'''
 
             prompt = PromptTemplate.from_template(template)
+
     """  # noqa: E501
     missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
         prompt.input_variables + list(prompt.partial_variables),

libs/langchain/langchain/agents/self_ask_with_search/base.py

@@ -185,6 +185,7 @@ def create_self_ask_with_search_agent(
             Are followup questions needed here:{agent_scratchpad}'''
 
             prompt = PromptTemplate.from_template(template)
+
     """  # noqa: E501
     missing_vars = {"agent_scratchpad"}.difference(
         prompt.input_variables + list(prompt.partial_variables),

libs/langchain/langchain/agents/structured_chat/base.py

@@ -280,6 +280,7 @@ def create_structured_chat_agent(
                     ("human", human),
                 ]
             )
+
     """  # noqa: E501
     missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
         prompt.input_variables + list(prompt.partial_variables),

libs/langchain/langchain/agents/tool_calling_agent/base.py

@@ -85,6 +85,7 @@ def create_tool_calling_agent(
         The agent prompt must have an `agent_scratchpad` key that is a
             ``MessagesPlaceholder``. Intermediate agent actions and tool output
             messages will be passed in here.
+
     """
     missing_vars = {"agent_scratchpad"}.difference(
         prompt.input_variables + list(prompt.partial_variables),

libs/langchain/langchain/agents/xml/base.py

@@ -37,7 +37,6 @@ class XMLAgent(BaseSingleActionAgent):
             tools = ...
             model =
 
-
     """
 
     tools: list[BaseTool]
@@ -209,6 +208,7 @@ def create_xml_agent(
             Question: {input}
             {agent_scratchpad}'''
             prompt = PromptTemplate.from_template(template)
+
     """  # noqa: E501
     missing_vars = {"tools", "agent_scratchpad"}.difference(
         prompt.input_variables + list(prompt.partial_variables),

libs/langchain/langchain/chains/api/base.py

@@ -191,6 +191,7 @@ try:
             )
             async for event in events:
                 event["messages"][-1].pretty_print()
+
         """  # noqa: E501
 
         api_request_chain: LLMChain

libs/langchain/langchain/chains/base.py

@@ -618,6 +618,7 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
                 context = "Weather report for Boise, Idaho on 07/03/23..."
                 chain.run(question=question, context=context)
                 # -> "The temperature in Boise is..."
+
         """
         # Run at start to make sure this is possible/defined
         _output_key = self._run_output_key
@@ -692,6 +693,7 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
                 context = "Weather report for Boise, Idaho on 07/03/23..."
                 await chain.arun(question=question, context=context)
                 # -> "The temperature in Boise is..."
+
         """
         if len(self.output_keys) != 1:
             msg = (
@@ -746,6 +748,7 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
 
                 chain.dict(exclude_unset=True)
                 # -> {"_type": "foo", "verbose": False, ...}
+
         """
         _dict = super().dict(**kwargs)
         with contextlib.suppress(NotImplementedError):
@@ -765,6 +768,7 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
             .. code-block:: python
 
                 chain.save(file_path="path/chain.yaml")
+
         """
         if self.memory is not None:
             msg = "Saving of memory is not yet supported."

libs/langchain/langchain/chains/combine_documents/base.py

@@ -234,6 +234,7 @@ class AnalyzeDocumentChain(Chain):
                     input_documents=itemgetter("input_document") | split_text,
                 ) | chain.pick("output_text")
             )
+
     """
 
     input_key: str = "input_document"  #: :meta private:

libs/langchain/langchain/chains/combine_documents/map_reduce.py

@@ -99,6 +99,7 @@ class MapReduceDocumentsChain(BaseCombineDocumentsChain):
                 llm_chain=llm_chain,
                 reduce_documents_chain=reduce_documents_chain,
             )
+
     """
 
     llm_chain: LLMChain

libs/langchain/langchain/chains/combine_documents/map_rerank.py

@@ -69,6 +69,7 @@ class MapRerankDocumentsChain(BaseCombineDocumentsChain):
                 rank_key="score",
                 answer_key="answer",
             )
+
     """
 
     llm_chain: LLMChain

libs/langchain/langchain/chains/combine_documents/reduce.py

@@ -201,6 +201,7 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain):
                 combine_documents_chain=combine_documents_chain,
                 collapse_documents_chain=collapse_documents_chain,
             )
+
     """
 
     combine_documents_chain: BaseCombineDocumentsChain

libs/langchain/langchain/chains/combine_documents/refine.py

@@ -79,6 +79,7 @@ class RefineDocumentsChain(BaseCombineDocumentsChain):
                 document_variable_name=document_variable_name,
                 initial_response_name=initial_response_name,
             )
+
     """
 
     initial_llm_chain: LLMChain

libs/langchain/langchain/chains/combine_documents/stuff.py

@@ -75,6 +75,7 @@ def create_stuff_documents_chain(
             ]
 
             chain.invoke({"context": docs})
+
     """  # noqa: E501
 
     _validate_prompt(prompt, document_variable_name)
@@ -142,6 +143,7 @@ class StuffDocumentsChain(BaseCombineDocumentsChain):
                 document_prompt=document_prompt,
                 document_variable_name=document_variable_name
             )
+
     """
 
     llm_chain: LLMChain

libs/langchain/langchain/chains/constitutional_ai/base.py

@@ -187,6 +187,7 @@ class ConstitutionalChain(Chain):
             )
 
             constitutional_chain.run(question="What is the meaning of life?")
+
     """  # noqa: E501
 
     chain: LLMChain

libs/langchain/langchain/chains/conversation/base.py

@@ -97,6 +97,7 @@ class ConversationChain(LLMChain):
             from langchain_community.llms import OpenAI
 
             conversation = ConversationChain(llm=OpenAI())
+
     """
 
     memory: BaseMemory = Field(default_factory=ConversationBufferMemory)

libs/langchain/langchain/chains/conversational_retrieval/base.py

@@ -374,6 +374,7 @@ class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
                 retriever=retriever,
                 question_generator=question_generator_chain,
             )
+
     """
 
     retriever: BaseRetriever

libs/langchain/langchain/chains/elasticsearch_database/base.py

@@ -34,6 +34,7 @@ class ElasticsearchDatabaseChain(Chain):
 
             database = Elasticsearch("http://localhost:9200")
             db_chain = ElasticsearchDatabaseChain.from_llm(OpenAI(), database)
+
     """
 
     query_chain: Runnable

libs/langchain/langchain/chains/llm.py

@@ -74,6 +74,7 @@ class LLMChain(Chain):
                 input_variables=["adjective"], template=prompt_template
             )
             llm = LLMChain(llm=OpenAI(), prompt=prompt)
+
     """
 
     @classmethod
@@ -323,6 +324,7 @@ class LLMChain(Chain):
             .. code-block:: python
 
                 completion = llm.predict(adjective="funny")
+
         """
         return self(kwargs, callbacks=callbacks)[self.output_key]
 
@@ -340,6 +342,7 @@ class LLMChain(Chain):
             .. code-block:: python
 
                 completion = llm.predict(adjective="funny")
+
         """
         return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]
 

libs/langchain/langchain/chains/llm_checker/base.py

@@ -82,6 +82,7 @@ class LLMCheckerChain(Chain):
             from langchain.chains import LLMCheckerChain
             llm = OpenAI(temperature=0.7)
             checker_chain = LLMCheckerChain.from_llm(llm)
+
     """
 
     question_to_checked_assertions_chain: SequentialChain

libs/langchain/langchain/chains/llm_math/base.py

@@ -146,6 +146,7 @@ class LLMMathChain(Chain):
             from langchain.chains import LLMMathChain
             from langchain_community.llms import OpenAI
             llm_math = LLMMathChain.from_llm(OpenAI())
+
     """  # noqa: E501
 
     llm_chain: LLMChain

libs/langchain/langchain/chains/llm_summarization_checker/base.py

@@ -85,6 +85,7 @@ class LLMSummarizationCheckerChain(Chain):
             from langchain.chains import LLMSummarizationCheckerChain
             llm = OpenAI(temperature=0.0)
             checker_chain = LLMSummarizationCheckerChain.from_llm(llm)
+
     """
 
     sequential_chain: SequentialChain

libs/langchain/langchain/chains/moderation.py

@@ -27,6 +27,7 @@ class OpenAIModerationChain(Chain):
 
             from langchain.chains import OpenAIModerationChain
             moderation = OpenAIModerationChain()
+
     """
 
     client: Any = None  #: :meta private:

libs/langchain/langchain/chains/natbot/base.py

@@ -47,6 +47,7 @@ class NatBotChain(Chain):
 
             from langchain.chains import NatBotChain
             natbot = NatBotChain.from_default("Buy me a new hat.")
+
     """
 
     llm_chain: Runnable
@@ -151,6 +152,7 @@ class NatBotChain(Chain):
 
                 browser_content = "...."
                 llm_command = natbot.run("www.google.com", browser_content)
+
         """
         _inputs = {
             self.input_url_key: url,

libs/langchain/langchain/chains/openai_functions/base.py

@@ -121,6 +121,7 @@ def create_openai_fn_chain(
                 chain = create_openai_fn_chain([RecordPerson, RecordDog], llm, prompt)
                 chain.run("Harry was a chubby brown beagle who loved chicken")
                 # -> RecordDog(name="Harry", color="brown", fav_food="chicken")
+
     """  # noqa: E501
     if not functions:
         msg = "Need to pass in at least one function. Received zero."
@@ -203,6 +204,7 @@ def create_structured_output_chain(
                 chain = create_structured_output_chain(Dog, llm, prompt)
                 chain.run("Harry was a chubby brown beagle who loved chicken")
                 # -> Dog(name="Harry", color="brown", fav_food="chicken")
+
     """  # noqa: E501
     if isinstance(output_schema, dict):
         function: Any = {

libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py

@@ -94,6 +94,7 @@ def create_citation_fuzzy_match_runnable(llm: BaseChatModel) -> Runnable:
 
     Returns:
         Runnable that can be used to answer questions with citations.
+
     """
     if llm.bind_tools is BaseChatModel.bind_tools:
         msg = "Language model must implement bind_tools to use this function."

libs/langchain/langchain/chains/openai_functions/openapi.py

@@ -345,6 +345,7 @@ def get_openapi_chain(
             `ChatOpenAI(model="gpt-3.5-turbo-0613")`.
         prompt: Main prompt template to use.
         request_chain: Chain for taking the functions output and executing the request.
+
     """  # noqa: E501
     try:
         from langchain_community.utilities.openapi import OpenAPISpec

libs/langchain/langchain/chains/openai_functions/tagging.py

@@ -86,6 +86,7 @@ def create_tagging_chain(
 
     Returns:
         Chain (LLMChain) that can be used to extract information from a passage.
+
     """
     function = _get_tagging_function(schema)
     prompt = prompt or ChatPromptTemplate.from_template(_TAGGING_TEMPLATE)
@@ -154,6 +155,7 @@ def create_tagging_chain_pydantic(
 
     Returns:
         Chain (LLMChain) that can be used to extract information from a passage.
+
     """
     if hasattr(pydantic_schema, "model_json_schema"):
         openai_schema = pydantic_schema.model_json_schema()

libs/langchain/langchain/chains/qa_generation/base.py

@@ -62,6 +62,7 @@ class QAGenerationChain(Chain):
                     split_text | RunnableEach(bound=prompt | llm | JsonOutputParser())
                 )
             )
+
     """
 
     llm_chain: LLMChain

libs/langchain/langchain/chains/retrieval_qa/base.py

@@ -147,6 +147,7 @@ class BaseRetrievalQA(Chain):
 
         res = indexqa({'query': 'This is my query'})
         answer, docs = res['result'], res['source_documents']
+
         """
         _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
         question = inputs[self.input_key]
@@ -191,6 +192,7 @@ class BaseRetrievalQA(Chain):
 
         res = indexqa({'query': 'This is my query'})
         answer, docs = res['result'], res['source_documents']
+
         """
         _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
         question = inputs[self.input_key]

libs/langchain/langchain/chains/router/llm_router.py

@@ -96,6 +96,7 @@ class LLMRouterChain(RouterChain):
             )
 
             chain.invoke({"query": "what color are carrots"})
+
     """  # noqa: E501
 
     llm_chain: LLMChain

libs/langchain/langchain/chains/router/multi_prompt.py

@@ -140,6 +140,7 @@ class MultiPromptChain(MultiRouteChain):
             result = await app.ainvoke({"query": "what color are carrots"})
             print(result["destination"])
             print(result["answer"])
+
     """  # noqa: E501
 
     @property

libs/langchain/langchain/chains/sql_database/query.py

@@ -113,6 +113,7 @@ def create_sql_query_chain(
 
             Question: {input}'''
             prompt = PromptTemplate.from_template(template)
+
     """  # noqa: E501
     if prompt is not None:
         prompt_to_use = prompt

libs/langchain/langchain/chains/structured_output/base.py

@@ -132,6 +132,7 @@ def create_openai_fn_runnable(
                 structured_llm = create_openai_fn_runnable([RecordPerson, RecordDog], llm)
                 structured_llm.invoke("Harry was a chubby brown beagle who loved chicken)
                 # -> RecordDog(name="Harry", color="brown", fav_food="chicken")
+
     """  # noqa: E501
     if not functions:
         msg = "Need to pass in at least one function. Received zero."
@@ -390,6 +391,7 @@ def create_structured_output_runnable(
                 )
                 chain = prompt | structured_llm
                 chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
+
     """  # noqa: E501
     # for backwards compatibility
     force_function_usage = kwargs.get(

libs/langchain/langchain/chains/transform.py

@@ -26,6 +26,7 @@ class TransformChain(Chain):
             from langchain.chains import TransformChain
             transform_chain = TransformChain(input_variables=["text"],
              output_variables["entities"], transform=func())
+
     """
 
     input_variables: list[str]

libs/langchain/langchain/embeddings/base.py

@@ -47,6 +47,7 @@ def _parse_model_string(model_name: str) -> tuple[str, str]:
     Raises:
         ValueError: If the model string is not in the correct format or
             the provider is unsupported
+
     """
     if ":" not in model_name:
         providers = _SUPPORTED_PROVIDERS
@@ -177,6 +178,7 @@ def init_embeddings(
             )
 
     .. versionadded:: 0.3.9
+
     """
     if not model:
         providers = _SUPPORTED_PROVIDERS.keys()

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

@@ -140,6 +140,7 @@ class TrajectoryEvalChain(AgentTrajectoryEvaluator, LLMEvalChain):
         )
         print(result["score"])  # noqa: T201
         # 0
+
     """
 
     agent_tools: Optional[list[BaseTool]] = None

libs/langchain/langchain/evaluation/loading.py

@@ -58,6 +58,7 @@ def load_dataset(uri: str) -> list[dict]:
 
         from langchain.evaluation import load_dataset
         ds = load_dataset("llm-math")
+
     """
     try:
         from datasets import load_dataset

libs/langchain/langchain/retrievers/document_compressors/listwise_rerank.py

@@ -70,6 +70,7 @@ class LLMListwiseRerank(BaseDocumentCompressor):
             compressed_docs = reranker.compress_documents(documents, "Who is steve")
             assert len(compressed_docs) == 3
             assert "Steve" in compressed_docs[0].page_content
+
     """
 
     reranker: Runnable[dict, list[Document]]

libs/langchain/langchain/retrievers/parent_document_retriever.py

@@ -54,6 +54,7 @@ class ParentDocumentRetriever(MultiVectorRetriever):
                 child_splitter=child_splitter,
                 parent_splitter=parent_splitter,
             )
+
     """  # noqa: E501
 
     child_splitter: TextSplitter

libs/langchain/langchain/smith/__init__.py

@@ -87,6 +87,7 @@ or LangSmith's `RunEvaluator` classes.
 - :func:`arun_on_dataset <langchain.smith.evaluation.runner_utils.arun_on_dataset>`: Asynchronous function to evaluate a chain, agent, or other LangChain component over a dataset.
 - :func:`run_on_dataset <langchain.smith.evaluation.runner_utils.run_on_dataset>`: Function to evaluate a chain, agent, or other LangChain component over a dataset.
 - :class:`RunEvalConfig <langchain.smith.evaluation.config.RunEvalConfig>`: Class representing the configuration for running evaluation. You can select evaluators by :class:`EvaluatorType <langchain.evaluation.schema.EvaluatorType>` or config, or you can pass in `custom_evaluators`
+
 """  # noqa: E501
 
 from langchain.smith.evaluation import (

libs/langchain/langchain/smith/evaluation/runner_utils.py

@@ -1451,6 +1451,7 @@ async def arun_on_dataset(
             llm_or_chain_factory=construct_chain,
             evaluation=evaluation_config,
         )
+
     """  # noqa: E501
     input_mapper = kwargs.pop("input_mapper", None)
     if input_mapper:
@@ -1623,6 +1624,7 @@ def run_on_dataset(
             llm_or_chain_factory=construct_chain,
             evaluation=evaluation_config,
         )
+
     """  # noqa: E501
     input_mapper = kwargs.pop("input_mapper", None)
     if input_mapper:

libs/langchain/langchain/storage/encoder_backed.py

@@ -46,6 +46,7 @@ class EncoderBackedStore(BaseStore[K, V]):
         store.mset([(1, 3.14), (2, 2.718)])
         values = store.mget([1, 2])  # Retrieves [3.14, 2.718]
         store.mdelete([1, 2])  # Deletes the keys 1 and 2
+
     """
 
     def __init__(