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)

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.
abatch_as_completed(inputs[, config, ...])	Run ainvoke in parallel on a list of inputs, yielding results as they complete.
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.
batch_as_completed(inputs[, config, ...])	Run invoke in parallel on a list of inputs, yielding results as they complete.
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 Runnable-like objects to make 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

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 abatch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]

Run ainvoke in parallel on a list of inputs, yielding results as they complete.

Parameters

• inputs (List[Input]) –

• config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

• return_exceptions (bool) –

• kwargs (Optional[Any]) –

Return type

AsyncIterator[Tuple[int, Union[Output, Exception]]]

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

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.

from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter

prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])

chain: Runnable = prompt | llm | {"str": StrOutputParser()}

chain_with_assign = chain.assign(hello=itemgetter("str") | llm)

print(chain_with_assign.input_schema.schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.schema()) #
{'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}

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]

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]

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]

batch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → Iterator[Tuple[int, Union[Output, Exception]]]

Run invoke in parallel on a list of inputs, yielding results as they complete.

Parameters

• inputs (List[Input]) –

• config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

• return_exceptions (bool) –

• kwargs (Optional[Any]) –

Return type

Iterator[Tuple[int, Union[Output, Exception]]]

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

Bind arguments to a Runnable, returning a new Runnable.

Useful when a runnable in a chain requires an argument that is not in the output of the previous runnable or included in the user input.

Example:

from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model='llama2')

# Without bind.
chain = (
    llm
    | StrOutputParser()
)

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'

# With bind.
chain = (
    llm.bind(stop=["three"])
    | StrOutputParser()
)

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'

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: Optional[RunnableConfig] = None) → Graph

Return a graph representation of this runnable.

Parameters

config (Optional[RunnableConfig]) –

Return type

Graph

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

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

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.

Example

from langchain_core.runnables import RunnableLambda

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

runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]

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.

Pick single key:

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}

json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]

Pick list of keys:

from typing import Any

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
def as_bytes(x: Any) -> bytes:
    return bytes(x, "utf-8")

chain = RunnableMap(
    str=as_str,
    json=as_json,
    bytes=RunnableLambda(as_bytes)
)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

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 Runnable-like objects to make a RunnableSequence.

Equivalent to RunnableSequence(self, *others) or self | others[0] | …

Example

from langchain_core.runnables import RunnableLambda

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

def mul_two(x: int) -> int:
    return x * 2

runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4

sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]

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]

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]

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.

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


def _generate_immediate_error(input: Iterator) -> Iterator[str]:
    raise ValueError()
    yield ""


def _generate(input: Iterator) -> Iterator[str]:
    yield from "foo bar"


runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
    [RunnableGenerator(_generate)]
    )
print(''.join(runnable.stream({}))) #foo bar

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.

Example:

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.

Example:

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

• Download the content

• Dynamically route logic based on input {#dynamically-route-logic-based-on-input}

• QA with private data protection {#qa-with-private-data-protection}

• Run custom functions {#run-custom-functions}

• Setup {#setup}

• Streaming With LangChain {#streaming-with-langchain}

• This will only get documents for Ankush

• Uncomment if you want to log to LangSmith

• os.environ[“LANGCHAIN_TRACING_V2”] = “true”

• retry.md

• 🦜️🏓 LangServe