langchain_core.runnables.base.RunnableGenerator¶
class langchain_core.runnables.base.RunnableGenerator(transform: Union[Callable[[Iterator[Input]], Iterator[Output]], Callable[[AsyncIterator[Input]], AsyncIterator[Output]]], atransform: Optional[Callable[[AsyncIterator[Input]], AsyncIterator[Output]]] = None)[source]¶
A runnable that runs a generator function.
Attributes
InputType
The type of input this runnable accepts specified as a type annotation.
OutputType
The type of output this runnable produces specified as a type annotation.
config_specs
List configurable fields for this runnable.
input_schema
The type of input this runnable accepts specified as a pydantic model.
output_schema
The type of output this runnable produces specified as a pydantic model.
Methods
__init__(transform[, atransform])
abatch(inputs[, config, return_exceptions])
Default implementation runs ainvoke in parallel using asyncio.gather.
ainvoke(input[, config])
Default implementation of ainvoke, calls invoke from a thread.
astream(input[, config])
Default implementation of astream, which calls ainvoke.
astream_log(input[, config, diff, ...])
Stream all output from a runnable, as reported to the callback system.
atransform(input[, config])
Default implementation of atransform, which buffers input and calls astream.
batch(inputs[, config, return_exceptions])
Default implementation runs invoke in parallel using a thread pool executor.
bind(**kwargs)
Bind arguments to a Runnable, returning a new Runnable.
config_schema(*[, include])
The type of config this runnable accepts specified as a pydantic model.
get_input_schema([config])
Get a pydantic model that can be used to validate input to the runnable.
get_output_schema([config])