style: some cleanup (#33857)

Author: mdrxy

libs/core/langchain_core/example_selectors/length_based.py

@@ -29,7 +29,7 @@ class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
     max_length: int = 2048
     """Max length for the prompt, beyond which examples are cut."""
 
-    example_text_lengths: list[int] = Field(default_factory=list)  # :meta private:
+    example_text_lengths: list[int] = Field(default_factory=list)
     """Length of each example."""
 
     def add_example(self, example: dict[str, str]) -> None:

libs/core/langchain_core/language_models/base.py

@@ -131,14 +131,19 @@ class BaseLanguageModel(
 
     Caching is not currently supported for streaming methods of models.
     """
+
     verbose: bool = Field(default_factory=_get_verbosity, exclude=True, repr=False)
     """Whether to print out response text."""
+
     callbacks: Callbacks = Field(default=None, exclude=True)
     """Callbacks to add to the run trace."""
+
     tags: list[str] | None = Field(default=None, exclude=True)
     """Tags to add to the run trace."""
+
     metadata: dict[str, Any] | None = Field(default=None, exclude=True)
     """Metadata to add to the run trace."""
+
     custom_get_token_ids: Callable[[str], list[int]] | None = Field(
         default=None, exclude=True
     )

libs/core/langchain_core/language_models/chat_models.py

@@ -1515,10 +1515,10 @@ def with_structured_output(
         Args:
             schema: The output schema. Can be passed in as:
 
-                - an OpenAI function/tool schema,
-                - a JSON Schema,
-                - a `TypedDict` class,
-                - or a Pydantic class.
+                - An OpenAI function/tool schema,
+                - A JSON Schema,
+                - A `TypedDict` class,
+                - Or a Pydantic class.
 
                 If `schema` is a Pydantic class then the model output will be a
                 Pydantic instance of that class, and the model-generated fields will be
@@ -1530,11 +1530,15 @@ def with_structured_output(
                 when specifying a Pydantic or `TypedDict` class.
 
             include_raw:
-                If `False` then only the parsed structured output is returned. If
-                an error occurs during model output parsing it will be raised. If `True`
-                then both the raw model response (a `BaseMessage`) and the parsed model
-                response will be returned. If an error occurs during output parsing it
-                will be caught and returned as well.
+                If `False` then only the parsed structured output is returned.
+
+                If an error occurs during model output parsing it will be raised.
+
+                If `True` then both the raw model response (a `BaseMessage`) and the
+                parsed model response will be returned.
+
+                If an error occurs during output parsing it will be caught and returned
+                as well.
 
                 The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
                 `'parsing_error'`.
@@ -1640,7 +1644,7 @@ class AnswerWithJustification(BaseModel):
         ```
 
         !!! warning "Behavior changed in `langchain-core` 0.2.26"
-            Added support for TypedDict class.
+            Added support for `TypedDict` class.
 
         """  # noqa: E501
         _ = kwargs.pop("method", None)

libs/core/langchain_core/runnables/base.py

@@ -829,8 +829,9 @@ def invoke(
 
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details.
+                do in parallel, and other keys.
+
+                Please refer to `RunnableConfig` for more details.
 
         Returns:
             The output of the `Runnable`.
@@ -850,8 +851,9 @@ async def ainvoke(
 
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details.
+                do in parallel, and other keys.
+
+                Please refer to `RunnableConfig` for more details.
 
         Returns:
             The output of the `Runnable`.
@@ -878,8 +880,9 @@ def batch(
             config: A config to use when invoking the `Runnable`. The config supports
                 standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work
-                to do in parallel, and other keys. Please refer to the
-                `RunnableConfig` for more details.
+                to do in parallel, and other keys.
+
+                Please refer to `RunnableConfig` for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
@@ -945,8 +948,9 @@ def batch_as_completed(
 
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details.
+                do in parallel, and other keys.
+
+                Please refer to `RunnableConfig` for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
@@ -1012,8 +1016,9 @@ async def abatch(
 
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details.
+                do in parallel, and other keys.
+
+                Please refer to `RunnableConfig` for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
@@ -1076,8 +1081,9 @@ async def abatch_as_completed(
 
                 The config supports standard keys like `'tags'`, `'metadata'` for
                 tracing purposes, `'max_concurrency'` for controlling how much work to
-                do in parallel, and other keys. Please refer to the `RunnableConfig`
-                for more details.
+                do in parallel, and other keys.
+
+                Please refer to `RunnableConfig` for more details.
             return_exceptions: Whether to return exceptions instead of raising them.
             **kwargs: Additional keyword arguments to pass to the `Runnable`.
 
@@ -1755,46 +1761,52 @@ def with_alisteners(
             import time
             import asyncio
 
+
             def format_t(timestamp: float) -> str:
                 return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()
 
+
             async def test_runnable(time_to_sleep: int):
                 print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
                 await asyncio.sleep(time_to_sleep)
                 print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")
 
+
             async def fn_start(run_obj: Runnable):
                 print(f"on start callback starts at {format_t(time.time())}")
                 await asyncio.sleep(3)
                 print(f"on start callback ends at {format_t(time.time())}")
 
+
             async def fn_end(run_obj: Runnable):
                 print(f"on end callback starts at {format_t(time.time())}")
                 await asyncio.sleep(2)
                 print(f"on end callback ends at {format_t(time.time())}")
 
+
             runnable = RunnableLambda(test_runnable).with_alisteners(
-                on_start=fn_start,
-                on_end=fn_end
+                on_start=fn_start, on_end=fn_end
             )
+
+
             async def concurrent_runs():
                 await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))
 
-            asyncio.run(concurrent_runs())
-            Result:
-            on start callback starts at 2025-03-01T07:05:22.875378+00:00
-            on start callback starts at 2025-03-01T07:05:22.875495+00:00
-            on start callback ends at 2025-03-01T07:05:25.878862+00:00
-            on start callback ends at 2025-03-01T07:05:25.878947+00:00
-            Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
-            Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
-            Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
-            on end callback starts at 2025-03-01T07:05:27.882360+00:00
-            Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
-            on end callback starts at 2025-03-01T07:05:28.882428+00:00
-            on end callback ends at 2025-03-01T07:05:29.883893+00:00
-            on end callback ends at 2025-03-01T07:05:30.884831+00:00
 
+            asyncio.run(concurrent_runs())
+            # Result:
+            # on start callback starts at 2025-03-01T07:05:22.875378+00:00
+            # on start callback starts at 2025-03-01T07:05:22.875495+00:00
+            # on start callback ends at 2025-03-01T07:05:25.878862+00:00
+            # on start callback ends at 2025-03-01T07:05:25.878947+00:00
+            # Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
+            # Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
+            # Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
+            # on end callback starts at 2025-03-01T07:05:27.882360+00:00
+            # Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
+            # on end callback starts at 2025-03-01T07:05:28.882428+00:00
+            # on end callback ends at 2025-03-01T07:05:29.883893+00:00
+            # on end callback ends at 2025-03-01T07:05:30.884831+00:00
             ```
         """
         return RunnableBinding(
@@ -1856,7 +1868,7 @@ def with_retry(
                 `exp_base`, and `jitter` (all `float` values).
 
         Returns:
-            A new Runnable that retries the original Runnable on exceptions.
+            A new `Runnable` that retries the original `Runnable` on exceptions.
 
         Example:
             ```python
@@ -1940,7 +1952,9 @@ def with_fallbacks(
             exceptions_to_handle: A tuple of exception types to handle.
             exception_key: If `string` is specified then handled exceptions will be
                 passed to fallbacks as part of the input under the specified key.
+
                 If `None`, exceptions will not be passed to fallbacks.
+
                 If used, the base `Runnable` and its fallbacks must accept a
                 dictionary as input.
 
@@ -1976,7 +1990,9 @@ def _generate(input: Iterator) -> Iterator[str]:
             exceptions_to_handle: A tuple of exception types to handle.
             exception_key: If `string` is specified then handled exceptions will be
                 passed to fallbacks as part of the input under the specified key.
+
                 If `None`, exceptions will not be passed to fallbacks.
+
                 If used, the base `Runnable` and its fallbacks must accept a
                 dictionary as input.
 
@@ -2442,10 +2458,14 @@ def as_tool(
 
         `as_tool` will instantiate a `BaseTool` with a name, description, and
         `args_schema` from a `Runnable`. Where possible, schemas are inferred
-        from `runnable.get_input_schema`. Alternatively (e.g., if the
-        `Runnable` takes a dict as input and the specific dict keys are not typed),
-        the schema can be specified directly with `args_schema`. You can also
-        pass `arg_types` to just specify the required arguments and their types.
+        from `runnable.get_input_schema`.
+
+        Alternatively (e.g., if the `Runnable` takes a dict as input and the specific
+        `dict` keys are not typed), the schema can be specified directly with
+        `args_schema`.
+
+        You can also pass `arg_types` to just specify the required arguments and their
+        types.
 
         Args:
             args_schema: The schema for the tool.
@@ -2514,7 +2534,7 @@ def f(x: dict[str, Any]) -> str:
         as_tool.invoke({"a": 3, "b": [1, 2]})
         ```
 
-        String input:
+        `str` input:
 
         ```python
         from langchain_core.runnables import RunnableLambda

libs/core/pyproject.toml

@@ -3,8 +3,13 @@ requires = ["hatchling"]
 build-backend = "hatchling.build"
 
 [project]
-authors = []
+name = "langchain-core"
+description = "Building applications with LLMs through composability"
 license = {text = "MIT"}
+readme = "README.md"
+authors = []
+
+version = "1.0.3"
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
     "langsmith>=0.3.45,<1.0.0",
@@ -15,10 +20,6 @@ dependencies = [
     "packaging>=23.2.0,<26.0.0",
     "pydantic>=2.7.4,<3.0.0",
 ]
-name = "langchain-core"
-version = "1.0.3"
-description = "Building applications with LLMs through composability"
-readme = "README.md"
 
 [project.urls]
 Homepage = "https://docs.langchain.com/"

libs/core/tests/unit_tests/pydantic_utils.py

@@ -105,7 +105,7 @@ def _remove_additionalproperties(schema: dict) -> dict[str, Any]:
     generating JSON schemas for dict properties with `Any` or `object` values.
 
     Pydantic 2.12 and later versions include `"additionalProperties": True` when
-    generating JSON schemas for TypedDict.
+    generating JSON schemas for `TypedDict`.
     """
     if isinstance(schema, dict):
         if (

libs/langchain/langchain_classic/agents/agent.py

@@ -105,10 +105,7 @@ async def aplan(
     @property
     @abstractmethod
     def input_keys(self) -> list[str]:
-        """Return the input keys.
-
-        :meta private:
-        """
+        """Return the input keys."""
 
     def return_stopped_response(
         self,
@@ -278,10 +275,7 @@ async def aplan(
     @property
     @abstractmethod
     def input_keys(self) -> list[str]:
-        """Return the input keys.
-
-        :meta private:
-        """
+        """Return the input keys."""
 
     def return_stopped_response(
         self,
@@ -819,10 +813,7 @@ def get_full_inputs(
 
     @property
     def input_keys(self) -> list[str]:
-        """Return the input keys.
-
-        :meta private:
-        """
+        """Return the input keys."""
         return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"})
 
     @model_validator(mode="after")
@@ -837,7 +828,7 @@ def validate_prompt(self) -> Self:
 
         Raises:
             ValueError: If `agent_scratchpad` is not in prompt.input_variables
-             and prompt is not a FewShotPromptTemplate or a PromptTemplate.
+                and prompt is not a FewShotPromptTemplate or a PromptTemplate.
         """
         prompt = self.llm_chain.prompt
         if "agent_scratchpad" not in prompt.input_variables:
@@ -1220,18 +1211,12 @@ def iter(
 
     @property
     def input_keys(self) -> list[str]:
-        """Return the input keys.
-
-        :meta private:
-        """
+        """Return the input keys."""
         return self._action_agent.input_keys
 
     @property
     def output_keys(self) -> list[str]:
-        """Return the singular output key.
-
-        :meta private:
-        """
+        """Return the singular output key."""
         if self.return_intermediate_steps:
             return [*self._action_agent.return_values, "intermediate_steps"]
         return self._action_agent.return_values

libs/langchain/langchain_classic/agents/openai_functions_agent/agent_token_buffer_memory.py

@@ -49,10 +49,7 @@ def buffer(self) -> list[BaseMessage]:
 
     @property
     def memory_variables(self) -> list[str]:
-        """Always return list of memory variables.
-
-        :meta private:
-        """
+        """Always return list of memory variables."""
         return [self.memory_key]
 
     @override