Runnable that selects which branch to run based on a condition.
The Runnable is initialized with a list of (condition, Runnable) pairs and
a default branch.
When operating on an input, the first condition that evaluates to True is
selected, and the corresponding Runnable is run on the input.
If no condition evaluates to True, the default branch is run on the input.
RunnableBranch(
self,
*branches: tuple[Runnable[Input, bool] | Callable[[Input], bool] | Callable[[Input], Awaitable[bool]], RunnableLike] | RunnableLike = ()
)Return True as this class is serializable.
Get the namespace of the LangChain object.
First evaluates the condition, then delegate to True or False branch.
First evaluates the condition, then delegate to True or False branch.
First evaluates the condition, then delegate to True or False branch.
Get a JSON schema that represents the input to the Runnable.
Get a JSON schema that represents the output of the Runnable.
The type of config this Runnable accepts specified as a Pydantic model.
Get a JSON schema that represents the config of the Runnable.
Return a list of prompts used by this Runnable.
Pipe Runnable objects.
Pick keys from the output dict of this Runnable.
Merge the Dict input with the output produced by the mapping argument.
Run invoke in parallel on a list of inputs.
Run ainvoke in parallel on a list of inputs.
Stream all output from a Runnable, as reported to the callback system.
Generate a stream of events.
Bind arguments to a Runnable, returning a new Runnable.
Bind lifecycle listeners to a Runnable, returning a new Runnable.
Bind async lifecycle listeners to a Runnable.
Bind input and output types to a Runnable, returning a new Runnable.
Create a new Runnable that retries the original Runnable on exceptions.
Map a function to multiple iterables.
Add fallbacks to a Runnable, returning a new Runnable.
Create a BaseTool from a Runnable.