Add documentation to Runnable (#11516)

libs/langchain/langchain/schema/runnable/base.py

@@ -77,11 +77,64 @@ Other = TypeVar("Other")
 
 
 class Runnable(Generic[Input, Output], ABC):
-    """A Runnable is a unit of work that can be invoked, batched, streamed, or
+    """A unit of work that can be invoked, batched, streamed, transformed and composed.
-    transformed."""
+
+    Key methods:
+
+    * invoke/ainvoke: Transforms a single input into an output.
+    * batch/abatch: Efficiently transforms multiple inputs into outputs.
+    * stream/astream: Streams output from a single input as it's produced.
+    * astream_log: Streams output and selected intermediate results from an input.
+
+    Batch: By default, batch runs invoke() in parallel using a thread pool executor.
+           Override to optimize batching.
+
+    Async: Methods with "a" suffix are asynchronous. By default, they execute
+           the sync counterpart using asyncio's thread pool. Override for native async.
+
+    All methods accept an optional config argument, which can be used to configure
+    execution, add tags and metadata for tracing and debugging etc.
+
+    Runnables expose schematic information about their input, output and config via
+    the input_schema property, the output_schema property and config_schema method.
+
+    The LangChain Expression Language (LCEL) is a declarative way to compose Runnables
+    into chains. Any chain constructed this way will automatically have sync, async,
+    batch, and streaming support.
+
+    The main composition primitives are RunnableSequence and RunnableParallel.
+
+    RunnableSequence invokes a series of runnables sequentially, with one runnable's
+    output serving as the next's input. Construct using the `|` operator or by
+    passing a list of runnables to RunnableSequence.
+
+    RunnableParallel invokes runnables concurrently, providing the same input
+    to each. Construct it using a dict literal within a sequence or by passing a
+    dict to RunnableParallel.
+
+    For example,
+
+    ..code-block:: python
+
+        from langchain.schema.runnable import RunnableLambda
+
+        # A RunnableSequence constructed using the `|` operator
+        sequence = RunnableLambda(lambda x: x + 1) | RunnableLambda(lambda x: x * 2)
+        sequence.invoke(1) # 4
+        sequence.batch([1, 2, 3]) # [4, 6, 8]
+
+
+        # A sequence that contains a RunnableParallel constructed using a dict literal
+        sequence = RunnableLambda(lambda x: x + 1) | {
+            'mul_2': RunnableLambda(lambda x: x * 2),
+            'mul_5': RunnableLambda(lambda x: x * 5)
+        }
+        sequence.invoke(1) # {'mul_2': 4, 'mul_5': 10}
+    """
 
     @property
     def InputType(self) -> Type[Input]:
+        """The type of input this runnable accepts specified as a type annotation."""
         for cls in self.__class__.__orig_bases__:  # type: ignore[attr-defined]
             type_args = get_args(cls)
             if type_args and len(type_args) == 2:
@@ -94,6 +147,7 @@ class Runnable(Generic[Input, Output], ABC):
 
     @property
     def OutputType(self) -> Type[Output]:
+        """The type of output this runnable produces specified as a type annotation."""
         for cls in self.__class__.__orig_bases__:  # type: ignore[attr-defined]
             type_args = get_args(cls)
             if type_args and len(type_args) == 2:
@@ -106,6 +160,7 @@ class Runnable(Generic[Input, Output], ABC):
 
     @property
     def input_schema(self) -> Type[BaseModel]:
+        """The type of input this runnable accepts specified as a pydantic model."""
         root_type = self.InputType
 
         if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
@@ -117,6 +172,7 @@ class Runnable(Generic[Input, Output], ABC):
 
     @property
     def output_schema(self) -> Type[BaseModel]:
+        """The type of output this runnable produces specified as a pydantic model."""
         root_type = self.OutputType
 
         if inspect.isclass(root_type) and issubclass(root_type, BaseModel):
@@ -128,9 +184,22 @@ class Runnable(Generic[Input, Output], ABC):
 
     @property
     def config_specs(self) -> Sequence[ConfigurableFieldSpec]:
+        """List configurable fields for this runnable."""
         return []
 
     def config_schema(self, *, include: Sequence[str]) -> Type[BaseModel]:
+        """The type of config this runnable accepts specified as a pydantic model.
+
+        To mark a field as configurable, see the `configurable_fields`
+        and `configurable_alternatives` methods.
+
+        Args:
+            include: A list of fields to include in the config schema.
+
+        Returns:
+            A pydantic model that can be used to validate config.
+        """
+
         class _Config:
             arbitrary_types_allowed = True
 
@@ -173,6 +242,7 @@ class Runnable(Generic[Input, Output], ABC):
             Mapping[str, Union[Runnable[Any, Other], Callable[[Any], Other], Any]],
         ],
     ) -> RunnableSequence[Input, Other]:
+        """Compose this runnable with another object to create a RunnableSequence."""
         return RunnableSequence(first=self, last=coerce_to_runnable(other))
 
     def __ror__(
@@ -184,12 +254,14 @@ class Runnable(Generic[Input, Output], ABC):
             Mapping[str, Union[Runnable[Other, Any], Callable[[Other], Any], Any]],
         ],
     ) -> RunnableSequence[Other, Output]:
+        """Compose this runnable with another object to create a RunnableSequence."""
         return RunnableSequence(first=coerce_to_runnable(other), last=self)
 
     """ --- Public API --- """
 
     @abstractmethod
     def invoke(self, input: Input, config: Optional[RunnableConfig] = None) -> Output:
+        """Transform a single input into an output. Override to implement."""
         ...
 
     async def ainvoke(