`langchain_core.runnables.base`.RunnableLambda¶

class langchain_core.runnables.base.RunnableLambda(func: Union[Union[Callable[[Input], Output], Callable[[Input], Iterator[Output]], Callable[[Input, RunnableConfig], Output], Callable[[Input, CallbackManagerForChainRun], Output], Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output]], Union[Callable[[Input], Awaitable[Output]], Callable[[Input], AsyncIterator[Output]], Callable[[Input, RunnableConfig], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]]], afunc: Optional[Union[Callable[[Input], Awaitable[Output]], Callable[[Input], AsyncIterator[Output]], Callable[[Input, RunnableConfig], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]]] = None, name: Optional[str] = None)[source]¶

RunnableLambda converts a python callable into a Runnable.

Wrapping a callable in a RunnableLambda makes the callable usable within either a sync or async context.

RunnableLambda can be composed as any other Runnable and provides seamless integration with LangChain tracing.

Examples

# This is a RunnableLambda
from langchain_core.runnables import RunnableLambda

def add_one(x: int) -> int:
    return x + 1

runnable = RunnableLambda(add_one)

runnable.invoke(1) # returns 2
runnable.batch([1, 2, 3]) # returns [2, 3, 4]

# Async is supported by default by delegating to the sync implementation
await runnable.ainvoke(1) # returns 2
await runnable.abatch([1, 2, 3]) # returns [2, 3, 4]


# Alternatively, can provide both synd and sync implementations
async def add_one_async(x: int) -> int:
    return x + 1

runnable = RunnableLambda(add_one, afunc=add_one_async)
runnable.invoke(1) # Uses add_one
await runnable.ainvoke(1) # Uses add_one_async

Create a RunnableLambda from a callable, and async callable or both.

Accepts both sync and async variants to allow providing efficient implementations for sync and async execution.

Parameters

func (Union[Union[Callable[[Input], Output], Callable[[Input], Iterator[Output]], Callable[[Input, RunnableConfig], Output], Callable[[Input, CallbackManagerForChainRun], Output], Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output]], Union[Callable[[Input], Awaitable[Output]], Callable[[Input], AsyncIterator[Output]], Callable[[Input, RunnableConfig], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]]]) – Either sync or async callable
afunc (Optional[Union[Callable[[Input], Awaitable[Output]], Callable[[Input], AsyncIterator[Output]], Callable[[Input, RunnableConfig], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]]]) – An async callable that takes an input and returns an output.
name (Optional[str]) –

Attributes

`InputType`	The type of the input to this runnable.
`OutputType`	The type of the output of this runnable as a type annotation.
`config_specs`	List configurable fields for this runnable.
`deps`	The dependencies of this runnable.
`input_schema`	The type of input this runnable accepts specified as a pydantic model.
`name`	The name of the runnable.
`output_schema`	The type of output this runnable produces specified as a pydantic model.

Methods

`__init__`(func[, afunc, name])	Create a RunnableLambda from a callable, and async callable or both.
`abatch`(inputs[, config, return_exceptions])	Default implementation runs ainvoke in parallel using asyncio.gather.
`ainvoke`(input[, config])	Invoke this runnable asynchronously.
`assign`(**kwargs)	Assigns new fields to the dict output of this runnable.
`astream`(input[, config])	Default implementation of astream, which calls ainvoke.
`astream_events`(input[, config, ...])	[Beta] Generate a stream of events.
`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_graph`([config])	Return a graph representation of this runnable.
`get_input_schema`([config])	The pydantic schema for the input to this runnable.
`get_name`([suffix, name])	Get the name of the runnable.
`get_output_schema`([config])	Get a pydantic model that can be used to validate output to the runnable.
`get_prompts`([config])
`invoke`(input[, config])	Invoke this runnable synchronously.
`map`()	Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
`pick`(keys)	Pick keys from the dict output of this runnable.
`pipe`(*others[, name])	Compose this runnable with another object to create a RunnableSequence.
`stream`(input[, config])	Default implementation of stream, which calls invoke.
`transform`(input[, config])	Default implementation of transform, which buffers input and then calls stream.
`with_config`([config])	Bind config to a Runnable, returning a new Runnable.
`with_fallbacks`(fallbacks, *[, ...])	Add fallbacks to a runnable, returning a new Runnable.
`with_listeners`(*[, on_start, on_end, on_error])	Bind lifecycle listeners to a Runnable, returning a new Runnable.
`with_retry`(*[, retry_if_exception_type, ...])	Create a new Runnable that retries the original runnable on exceptions.
`with_types`(*[, input_type, output_type])	Bind input and output types to a Runnable, returning a new Runnable.

__init__(func: Union[Union[Callable[[Input], Output], Callable[[Input], Iterator[Output]], Callable[[Input, RunnableConfig], Output], Callable[[Input, CallbackManagerForChainRun], Output], Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output]], Union[Callable[[Input], Awaitable[Output]], Callable[[Input], AsyncIterator[Output]], Callable[[Input, RunnableConfig], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]]], afunc: Optional[Union[Callable[[Input], Awaitable[Output]], Callable[[Input], AsyncIterator[Output]], Callable[[Input, RunnableConfig], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]]] = None, name: Optional[str] = None) → None[source]¶

Create a RunnableLambda from a callable, and async callable or both.

Accepts both sync and async variants to allow providing efficient implementations for sync and async execution.

Parameters

func (Union[Union[Callable[[Input], Output], Callable[[Input], Iterator[Output]], Callable[[Input, RunnableConfig], Output], Callable[[Input, CallbackManagerForChainRun], Output], Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output]], Union[Callable[[Input], Awaitable[Output]], Callable[[Input], AsyncIterator[Output]], Callable[[Input, RunnableConfig], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]]]) – Either sync or async callable
afunc (Optional[Union[Callable[[Input], Awaitable[Output]], Callable[[Input], AsyncIterator[Output]], Callable[[Input, RunnableConfig], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]], Callable[[Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]]]) – An async callable that takes an input and returns an output.
name (Optional[str]) –

Return type

None

async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶

Default implementation runs ainvoke in parallel using asyncio.gather.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying runnable uses an API which supports a batch mode.

Parameters

inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –

Return type

List[Output]

async ainvoke(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Output[source]¶

Invoke this runnable asynchronously.

Parameters

input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

Output

assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) → RunnableSerializable[Any, Any]¶

Assigns new fields to the dict output of this runnable. Returns a new runnable.

Parameters: kwargs (Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) –
Return type: RunnableSerializable[Any, Any]

async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output][source]¶

Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

Parameters

input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

AsyncIterator[Output]

astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → AsyncIterator[StreamEvent]¶

[Beta] Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: str - Event names are of the
format: on_[runnable_type]_(start|stream|end).
name: str - The name of the runnable that generated the event.
run_id: str - randomly generated ID associated with the given execution of
the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a parent runnable is assigned its own unique ID.
tags: Optional[List[str]] - The tags of the runnable that generated
the event.
metadata: Optional[Dict[str, Any]] - The metadata of the runnable
that generated the event.
data: Dict[str, Any]

Below is a table that illustrates some evens that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

event	name	chunk	input	output
on_chat_model_start	[model name]		{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream	[model name]	AIMessageChunk(content=”hello”)
on_chat_model_end	[model name]		{“messages”: [[SystemMessage, HumanMessage]]}	{“generations”: […], “llm_output”: None, …}
on_llm_start	[model name]		{‘input’: ‘hello’}
on_llm_stream	[model name]	‘Hello’
on_llm_end	[model name]		‘Hello human!’
on_chain_start	format_docs
on_chain_stream	format_docs	“hello world!, goodbye world!”
on_chain_end	format_docs		[Document(…)]	“hello world!, goodbye world!”
on_tool_start	some_tool		{“x”: 1, “y”: “2”}
on_tool_stream	some_tool	{“x”: 1, “y”: “2”}
on_tool_end	some_tool			{“x”: 1, “y”: “2”}
on_retriever_start	[retriever name]		{“query”: “hello”}
on_retriever_chunk	[retriever name]	{documents: […]}
on_retriever_end	[retriever name]		{“query”: “hello”}	{documents: […]}
on_prompt_start	[template_name]		{“question”: “hello”}
on_prompt_end	[template_name]		{“question”: “hello”}	ChatPromptValue(messages: [SystemMessage, …])

Here are declarations associated with the events shown above:

format_docs:

def format_docs(docs: List[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])

format_docs = RunnableLambda(format_docs)

some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}

prompt:

template = ChatPromptTemplate.from_messages(
    [("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})

Example:

from langchain_core.runnables import RunnableLambda

async def reverse(s: str) -> str:
    return s[::-1]

chain = RunnableLambda(func=reverse)

events = [
    event async for event in chain.astream_events("hello", version="v1")
]

# will produce the following events (run_id has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]

Parameters

input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
version (Literal['v1']) – The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.
include_names (Optional[Sequence[str]]) – Only include events from runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – Exclude events from runnables with matching tags.
kwargs (Any) – Additional keyword arguments to pass to the runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.

Returns

An async stream of StreamEvents.

Return type

AsyncIterator[StreamEvent]

Notes

async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶

Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc.

Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.

The jsonpatch ops can be applied in order to construct state.

Parameters

input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
diff (bool) – Whether to yield diffs between each step, or the current state.
with_streamed_output_list (bool) – Whether to yield the streamed_output list.
include_names (Optional[Sequence[str]]) – Only include logs with these names.
include_types (Optional[Sequence[str]]) – Only include logs with these types.
include_tags (Optional[Sequence[str]]) – Only include logs with these tags.
exclude_names (Optional[Sequence[str]]) – Exclude logs with these names.
exclude_types (Optional[Sequence[str]]) – Exclude logs with these types.
exclude_tags (Optional[Sequence[str]]) – Exclude logs with these tags.
kwargs (Any) –

Return type

Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]

async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output][source]¶

Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if they can start producing output while input is still being generated.

Parameters

input (AsyncIterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

AsyncIterator[Output]

batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶

Default implementation runs invoke in parallel using a thread pool executor.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying runnable uses an API which supports a batch mode.

Parameters

inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –

Return type

List[Output]

bind(**kwargs: Any) → Runnable[Input, Output]¶

Bind arguments to a Runnable, returning a new Runnable.

Parameters: kwargs (Any) –
Return type: Runnable[Input, Output]

config_schema(*, include: Optional[Sequence[str]] = None) → 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.

Parameters: include (Optional[Sequence[str]]) – A list of fields to include in the config schema.
Returns: A pydantic model that can be used to validate config.
Return type: Type[BaseModel]

get_graph(config: langchain_core.runnables.config.RunnableConfig | None = None) → Graph[source]¶

Return a graph representation of this runnable.

Parameters: config (langchain_core.runnables.config.RunnableConfig | None) –
Return type: Graph

get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel][source]¶

The pydantic schema for the input to this runnable.

Parameters: config (Optional[RunnableConfig]) –
Return type: Type[BaseModel]

get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶

Get the name of the runnable.

Parameters

suffix (Optional[str]) –
name (Optional[str]) –

Return type

str

get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶

Get a pydantic model that can be used to validate output to the runnable.

Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with.

This method allows to get an output schema for a specific configuration.

Parameters: config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns: A pydantic model that can be used to validate output.
Return type: Type[BaseModel]

get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶

Parameters: config (Optional[RunnableConfig]) –
Return type: List[BasePromptTemplate]

invoke(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Output[source]¶

Invoke this runnable synchronously.

Parameters

input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

Output

map() → Runnable[List[Input], List[Output]]¶

Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.

Return type: Runnable[List[Input], List[Output]]

pick(keys: Union[str, List[str]]) → RunnableSerializable[Any, Any]¶

Pick keys from the dict output of this runnable. Returns a new runnable.

Parameters: keys (Union[str, List[str]]) –
Return type: RunnableSerializable[Any, Any]

pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]¶

Compose this runnable with another object to create a RunnableSequence.

Parameters

others (Union[Runnable[Any, Other], Callable[[Any], Other]]) –
name (Optional[str]) –

Return type

RunnableSerializable[Input, Other]

stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output][source]¶

Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

Parameters

input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

Iterator[Output]

transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output][source]¶

Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.

Parameters

input (Iterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

Iterator[Output]

with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶

Bind config to a Runnable, returning a new Runnable.

Parameters

config (Optional[RunnableConfig]) –
kwargs (Any) –

Return type

Runnable[Input, Output]

with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) → RunnableWithFallbacksT[Input, Output]¶

Add fallbacks to a runnable, returning a new Runnable.

Parameters

fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle (Tuple[Type[BaseException], ...]) – A tuple of exception types to handle.
exception_key (Optional[str]) – 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.

Returns

A new Runnable that will try the original runnable, and then each fallback in order, upon failures.

Return type

RunnableWithFallbacksT[Input, Output]

with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶

Bind lifecycle listeners to a Runnable, returning a new Runnable.

on_start: Called before the runnable starts running, with the Run object. on_end: Called after the runnable finishes running, with the Run object. on_error: Called if the runnable throws an error, with the Run object.

The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.

Parameters

on_start (Optional[Listener]) –
on_end (Optional[Listener]) –
on_error (Optional[Listener]) –

Return type

Runnable[Input, Output]

with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶

Create a new Runnable that retries the original runnable on exceptions.

Parameters

retry_if_exception_type (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on
wait_exponential_jitter (bool) – Whether to add jitter to the wait time between retries
stop_after_attempt (int) – The maximum number of attempts to make before giving up

Returns

A new Runnable that retries the original runnable on exceptions.

Return type

Runnable[Input, Output]

with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶

Bind input and output types to a Runnable, returning a new Runnable.

Parameters

input_type (Optional[Type[Input]]) –
output_type (Optional[Type[Output]]) –

Return type

Runnable[Input, Output]

Examples using RunnableLambda¶

langchain_core.runnables.base.RunnableLambda¶

Examples using RunnableLambda¶

`langchain_core.runnables.base`.RunnableLambda¶