langchain-cohere

Reference docs

This page contains reference documentation for Cohere. See the docs for conceptual guides, tutorials, and examples on using Cohere modules.

langchain_cohere

FUNCTION	DESCRIPTION
create_cohere_react_agent	Create an agent that enables multiple tools to be used in sequence to complete a

ChatCohere

Bases: BaseChatModel, BaseCohere

Implements the BaseChatModel (and BaseLanguageModel) interface with Cohere's large language models.

Find out more about us at https://cohere.com and https://huggingface.co/CohereForAI

This implementation uses the Chat API - see https://docs.cohere.com/reference/chat

To use this you'll need to a Cohere API key - either pass it to cohere_api_key parameter or set the COHERE_API_KEY environment variable.

API keys are available on https://cohere.com - it's free to sign up and trial API keys work with this implementation.

Basic Example

from langchain_cohere import ChatCohere
from langchain_core.messages import HumanMessage

llm = ChatCohere(cohere_api_key="{API KEY}")

message = [HumanMessage(content="Hello, can you introduce yourself?")]

print(llm.invoke(message).content)

METHOD	DESCRIPTION
get_name	Get the name of the Runnable.
get_input_schema	Get a Pydantic model that can be used to validate input to the Runnable.
get_input_jsonschema	Get a JSON schema that represents the input to the Runnable.
get_output_schema	Get a Pydantic model that can be used to validate output to the Runnable.
get_output_jsonschema	Get a JSON schema that represents the output of the Runnable.
config_schema	The type of config this Runnable accepts specified as a Pydantic model.
get_config_jsonschema	Get a JSON schema that represents the config of the Runnable.
get_graph	Return a graph representation of this Runnable.
get_prompts	Return a list of prompts used by this Runnable.
__or__	Runnable "or" operator.
__ror__	Runnable "reverse-or" operator.
pipe	Pipe Runnable objects.
pick	Pick keys from the output dict of this Runnable.
assign	Assigns new fields to the dict output of this Runnable.
invoke	Transform a single input into an output.
ainvoke	Transform a single input into an output.
batch	Default implementation runs invoke in parallel using a thread pool executor.
batch_as_completed	Run invoke in parallel on a list of inputs.
abatch	Default implementation runs ainvoke in parallel using asyncio.gather.
abatch_as_completed	Run ainvoke in parallel on a list of inputs.
stream	Default implementation of stream, which calls invoke.
astream	Default implementation of astream, which calls ainvoke.
astream_log	Stream all output from a Runnable, as reported to the callback system.
astream_events	Generate a stream of events.
transform	Transform inputs to outputs.
atransform	Transform inputs to outputs.
bind	Bind arguments to a Runnable, returning a new Runnable.
with_config	Bind config to a Runnable, returning a new Runnable.
with_listeners	Bind lifecycle listeners to a Runnable, returning a new Runnable.
with_alisteners	Bind async lifecycle listeners to a Runnable.
with_types	Bind input and output types to a Runnable, returning a new Runnable.
with_retry	Create a new Runnable that retries the original Runnable on exceptions.
map	Return a new Runnable that maps a list of inputs to a list of outputs.
with_fallbacks	Add fallbacks to a Runnable, returning a new Runnable.
as_tool	Create a BaseTool from a Runnable.
__init__
is_lc_serializable	Is this class serializable?
get_lc_namespace	Get the namespace of the LangChain object.
lc_id	Return a unique identifier for this class for serialization purposes.
to_json	Serialize the Runnable to JSON.
to_json_not_implemented	Serialize a "not implemented" object.
validate_environment	Validate that api key and python package exists in environment.
configurable_fields	Configure particular Runnable fields at runtime.
configurable_alternatives	Configure alternatives for Runnable objects that can be set at runtime.
set_verbose	If verbose is None, set it.
generate_prompt	Pass a sequence of prompts to the model and return model generations.
agenerate_prompt	Asynchronously pass a sequence of prompts and return model generations.
get_token_ids	Return the ordered IDs of the tokens in a text.
get_num_tokens_from_messages	Get the number of tokens in the messages.
generate	Pass a sequence of prompts to the model and return model generations.
agenerate	Asynchronously pass a sequence of prompts to a model and return generations.
dict	Return a dictionary of the LLM.
bind_tools	Bind tools to the model.
with_structured_output	Model wrapper that returns outputs formatted to match the given schema.
get_num_tokens	Calculate number of tokens.

name class-attribute instance-attribute

name: str | None = None

The name of the Runnable. Used for debugging and tracing.

InputType property

InputType: TypeAlias

Get the input type for this Runnable.

OutputType property

OutputType: Any

Get the output type for this Runnable.

input_schema property

input_schema: type[BaseModel]

The type of input this Runnable accepts specified as a Pydantic model.

output_schema property

output_schema: type[BaseModel]

Output schema.

The type of output this Runnable produces specified as a Pydantic model.

config_specs property

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

lc_secrets property

lc_secrets: dict[str, str]

A map of constructor argument names to secret ids.

For example, {"openai_api_key": "OPENAI_API_KEY"}

lc_attributes property

lc_attributes: dict

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor.

Default is an empty dictionary.

model class-attribute instance-attribute

model: str | None = Field(default=None)

Model name to use.

temperature class-attribute instance-attribute

temperature: float | None = None

A non-negative float that tunes the degree of randomness in generation.

cohere_api_key class-attribute instance-attribute

cohere_api_key: SecretStr | None = Field(
    default_factory=secret_from_env("COHERE_API_KEY", default=None)
)

Cohere API key. If not provided, will be read from the environment variable.

streaming class-attribute instance-attribute

streaming: bool = Field(default=False)

Whether to stream the results.

user_agent class-attribute instance-attribute

user_agent: str = 'langchain:partner'

Identifier for the application making the request.

timeout_seconds class-attribute instance-attribute

timeout_seconds: float | None = 300

Timeout in seconds for the Cohere API request.

base_url class-attribute instance-attribute

base_url: str | None = None

Override the default Cohere API URL.

cache class-attribute instance-attribute

cache: BaseCache | bool | None = Field(default=None, exclude=True)

Whether to cache the response.

• If True, will use the global cache.
• If False, will not use a cache
• If None, will use the global cache if it's set, otherwise no cache.
• If instance of BaseCache, will use the provided cache.

Caching is not currently supported for streaming methods of models.

verbose class-attribute instance-attribute

verbose: bool = Field(default_factory=_get_verbosity, exclude=True, repr=False)

Whether to print out response text.

callbacks class-attribute instance-attribute

callbacks: Callbacks = Field(default=None, exclude=True)

Callbacks to add to the run trace.

tags class-attribute instance-attribute

tags: list[str] | None = Field(default=None, exclude=True)

Tags to add to the run trace.

metadata class-attribute instance-attribute

metadata: dict[str, Any] | None = Field(default=None, exclude=True)

Metadata to add to the run trace.

custom_get_token_ids class-attribute instance-attribute

custom_get_token_ids: Callable[[str], list[int]] | None = Field(
    default=None, exclude=True
)

Optional encoder to use for counting tokens.

rate_limiter class-attribute instance-attribute

rate_limiter: BaseRateLimiter | None = Field(default=None, exclude=True)

An optional rate limiter to use for limiting the number of requests.

disable_streaming class-attribute instance-attribute

disable_streaming: bool | Literal['tool_calling'] = False

Whether to disable streaming for this model.

If streaming is bypassed, then stream/astream/astream_events will defer to invoke/ainvoke.

• If True, will always bypass streaming case.
• If 'tool_calling', will bypass streaming case only when the model is called with a tools keyword argument. In other words, LangChain will automatically switch to non-streaming behavior (invoke) only when the tools argument is provided. This offers the best of both worlds.
• If False (Default), will always use streaming case if available.

The main reason for this flag is that code might be written using stream and a user may want to swap out a given model for another model whose the implementation does not properly support streaming.

output_version class-attribute instance-attribute

output_version: str | None = Field(
    default_factory=from_env("LC_OUTPUT_VERSION", default=None)
)

Version of AIMessage output format to store in message content.

AIMessage.content_blocks will lazily parse the contents of content into a standard format. This flag can be used to additionally store the standard format in message content, e.g., for serialization purposes.

Supported values:

• 'v0': provider-specific format in content (can lazily-parse with content_blocks)
• 'v1': standardized format in content (consistent with content_blocks)

Partner packages (e.g., langchain-openai) can also use this field to roll out new content formats in a backward-compatible way.

Added in langchain-core 1.0

profile property

profile: ModelProfile

Return profiling information for the model.

This property relies on the langchain-model-profiles package to retrieve chat model capabilities, such as context window sizes and supported features.

RAISES	DESCRIPTION
ImportError	If langchain-model-profiles is not installed.

RETURNS	DESCRIPTION
ModelProfile	A ModelProfile object containing profiling information for the model.

get_name

get_name(suffix: str | None = None, *, name: str | None = None) -> str

Get the name of the Runnable.

PARAMETER	DESCRIPTION
suffix	An optional suffix to append to the name. TYPE: str | None DEFAULT: None
name	An optional name to use instead of the Runnable's name. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
str	The name of the Runnable.

get_input_schema

get_input_schema(config: RunnableConfig | None = None) -> type[BaseModel]

Get a Pydantic model that can be used to validate input to the Runnable.

Runnable objects that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the Runnable is invoked with.

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

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
type[BaseModel]	A Pydantic model that can be used to validate input.

get_input_jsonschema

get_input_jsonschema(config: RunnableConfig | None = None) -> dict[str, Any]

Get a JSON schema that represents the input to the Runnable.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
dict[str, Any]	A JSON schema that represents the input to the Runnable.

Example

from langchain_core.runnables import RunnableLambda


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


runnable = RunnableLambda(add_one)

print(runnable.get_input_jsonschema())

Added in langchain-core 0.3.0

get_output_schema

get_output_schema(config: RunnableConfig | None = None) -> type[BaseModel]

Get a Pydantic model that can be used to validate output to the Runnable.

Runnable objects 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.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
type[BaseModel]	A Pydantic model that can be used to validate output.

get_output_jsonschema

get_output_jsonschema(config: RunnableConfig | None = None) -> dict[str, Any]

Get a JSON schema that represents the output of the Runnable.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
dict[str, Any]	A JSON schema that represents the output of the Runnable.

Example

from langchain_core.runnables import RunnableLambda


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


runnable = RunnableLambda(add_one)

print(runnable.get_output_jsonschema())

Added in langchain-core 0.3.0

config_schema

config_schema(*, include: Sequence[str] | None = 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.

PARAMETER	DESCRIPTION
include	A list of fields to include in the config schema. TYPE: Sequence[str] | None DEFAULT: None

RETURNS	DESCRIPTION
type[BaseModel]	A Pydantic model that can be used to validate config.

get_config_jsonschema

get_config_jsonschema(*, include: Sequence[str] | None = None) -> dict[str, Any]

Get a JSON schema that represents the config of the Runnable.

PARAMETER	DESCRIPTION
include	A list of fields to include in the config schema. TYPE: Sequence[str] | None DEFAULT: None

RETURNS	DESCRIPTION
dict[str, Any]	A JSON schema that represents the config of the Runnable.

Added in langchain-core 0.3.0

get_graph

get_graph(config: RunnableConfig | None = None) -> Graph

Return a graph representation of this Runnable.

get_prompts

get_prompts(config: RunnableConfig | None = None) -> list[BasePromptTemplate]

Return a list of prompts used by this Runnable.

__or__

__or__(
    other: Runnable[Any, Other]
    | Callable[[Iterator[Any]], Iterator[Other]]
    | Callable[[AsyncIterator[Any]], AsyncIterator[Other]]
    | Callable[[Any], Other]
    | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any],
) -> RunnableSerializable[Input, Other]

Runnable "or" operator.

Compose this Runnable with another object to create a RunnableSequence.

PARAMETER	DESCRIPTION
other	Another Runnable or a Runnable-like object. TYPE: Runnable[Any, Other] | Callable[[Iterator[Any]], Iterator[Other]] | Callable[[AsyncIterator[Any]], AsyncIterator[Other]] | Callable[[Any], Other] | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any]

RETURNS	DESCRIPTION
RunnableSerializable[Input, Other]	A new Runnable.

__ror__

__ror__(
    other: Runnable[Other, Any]
    | Callable[[Iterator[Other]], Iterator[Any]]
    | Callable[[AsyncIterator[Other]], AsyncIterator[Any]]
    | Callable[[Other], Any]
    | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any],
) -> RunnableSerializable[Other, Output]

Runnable "reverse-or" operator.

Compose this Runnable with another object to create a RunnableSequence.

PARAMETER	DESCRIPTION
other	Another Runnable or a Runnable-like object. TYPE: Runnable[Other, Any] | Callable[[Iterator[Other]], Iterator[Any]] | Callable[[AsyncIterator[Other]], AsyncIterator[Any]] | Callable[[Other], Any] | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any]

RETURNS	DESCRIPTION
RunnableSerializable[Other, Output]	A new Runnable.

pipe

pipe(
    *others: Runnable[Any, Other] | Callable[[Any], Other], name: str | None = None
) -> RunnableSerializable[Input, Other]

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
*others	Other Runnable or Runnable-like objects to compose TYPE: Runnable[Any, Other] | Callable[[Any], Other] DEFAULT: ()
name	An optional name for the resulting RunnableSequence. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
RunnableSerializable[Input, Other]	A new Runnable.

pick

pick(keys: str | list[str]) -> RunnableSerializable[Any, Any]

Pick keys from the output dict of this Runnable.

Pick a 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 a 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]"}

PARAMETER	DESCRIPTION
keys	A key or list of keys to pick from the output dict. TYPE: str | list[str]

RETURNS	DESCRIPTION
RunnableSerializable[Any, Any]	a new Runnable.

assign

assign(
    **kwargs: Runnable[dict[str, Any], Any]
    | Callable[[dict[str, Any]], Any]
    | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]],
) -> RunnableSerializable[Any, Any]

Assigns new fields to the dict output of this Runnable.

from langchain_core.language_models.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}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

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

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

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

PARAMETER	DESCRIPTION
**kwargs	A mapping of keys to Runnable or Runnable-like objects that will be invoked with the entire output dict of this Runnable. TYPE: Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any] | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]] DEFAULT: {}

RETURNS	DESCRIPTION
RunnableSerializable[Any, Any]	A new Runnable.

invoke

invoke(
    input: LanguageModelInput,
    config: RunnableConfig | None = None,
    *,
    stop: list[str] | None = None,
    **kwargs: Any,
) -> AIMessage

Transform a single input into an output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
Output	The output of the Runnable.

ainvoke async

ainvoke(
    input: LanguageModelInput,
    config: RunnableConfig | None = None,
    *,
    stop: list[str] | None = None,
    **kwargs: Any,
) -> AIMessage

Transform a single input into an output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
Output	The output of the Runnable.

batch

batch(
    inputs: list[Input],
    config: RunnableConfig | list[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> 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 must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: list[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | list[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

RETURNS	DESCRIPTION
list[Output]	A list of outputs from the Runnable.

batch_as_completed

batch_as_completed(
    inputs: Sequence[Input],
    config: RunnableConfig | Sequence[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> Iterator[tuple[int, Output | Exception]]

Run invoke in parallel on a list of inputs.

Yields results as they complete.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: Sequence[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | Sequence[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
tuple[int, Output | Exception]	Tuples of the index of the input and the output from the Runnable.

abatch async

abatch(
    inputs: list[Input],
    config: RunnableConfig | list[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> list[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

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

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

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: list[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | list[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

RETURNS	DESCRIPTION
list[Output]	A list of outputs from the Runnable.

abatch_as_completed async

abatch_as_completed(
    inputs: Sequence[Input],
    config: RunnableConfig | Sequence[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> AsyncIterator[tuple[int, Output | Exception]]

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: Sequence[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | Sequence[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[tuple[int, Output | Exception]]	A tuple of the index of the input and the output from the Runnable.

stream

stream(
    input: LanguageModelInput,
    config: RunnableConfig | None = None,
    *,
    stop: list[str] | None = None,
    **kwargs: Any,
) -> Iterator[AIMessageChunk]

Default implementation of stream, which calls invoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
Output	The output of the Runnable.

astream async

astream(
    input: LanguageModelInput,
    config: RunnableConfig | None = None,
    *,
    stop: list[str] | None = None,
    **kwargs: Any,
) -> AsyncIterator[AIMessageChunk]

Default implementation of astream, which calls ainvoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[Output]	The output of the Runnable.

astream_log async

astream_log(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    diff: bool = True,
    with_streamed_output_list: bool = True,
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any,
) -> 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.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Any
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
diff	Whether to yield diffs between each step or the current state. TYPE: bool DEFAULT: True
with_streamed_output_list	Whether to yield the streamed_output list. TYPE: bool DEFAULT: True
include_names	Only include logs with these names. TYPE: Sequence[str] | None DEFAULT: None
include_types	Only include logs with these types. TYPE: Sequence[str] | None DEFAULT: None
include_tags	Only include logs with these tags. TYPE: Sequence[str] | None DEFAULT: None
exclude_names	Exclude logs with these names. TYPE: Sequence[str] | None DEFAULT: None
exclude_types	Exclude logs with these types. TYPE: Sequence[str] | None DEFAULT: None
exclude_tags	Exclude logs with these tags. TYPE: Sequence[str] | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]	A RunLogPatch or RunLog object.

astream_events async

astream_events(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    version: Literal["v1", "v2"] = "v2",
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any,
) -> AsyncIterator[StreamEvent]

Generate a stream of events.

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

A StreamEvent is a dictionary with the following schema:

• event: Event names are of the format: on_[runnable_type]_(start|stream|end).
• name: The name of the Runnable that generated the event.
• run_id: 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.
• parent_ids: The IDs of the parent runnables that generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
• tags: The tags of the Runnable that generated the event.
• metadata: The metadata of the Runnable that generated the event.
• data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

Below is a table that illustrates some events 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.

Note

This reference table is for the v2 version of the schema.

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]]}	AIMessageChunk(content="hello world")
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_end	'some_tool'			{"x": 1, "y": "2"}
on_retriever_start	'[retriever name]'		{"query": "hello"}
on_retriever_end	'[retriever name]'		{"query": "hello"}	[Document(...), ..]
on_prompt_start	'[template_name]'		{"question": "hello"}
on_prompt_end	'[template_name]'		{"question": "hello"}	ChatPromptValue(messages: [SystemMessage, ...])

In addition to the standard events, users can also dispatch custom events (see example below).

Custom events will be only be surfaced with in the v2 version of the API!

A custom event has following format:

Attribute	Type	Description
name	str	A user defined name for the event.
data	Any	The data associated with the event. This can be anything, though we suggest making it JSON serializable.

Here are declarations associated with the standard 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"]})

For instance:

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="v2")]

# Will produce the following events
# (run_id, and parent_ids 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": [],
    },
]

Example: Dispatch Custom Event

from langchain_core.callbacks.manager import (
    adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio


async def slow_thing(some_input: str, config: RunnableConfig) -> str:
    """Do something that takes a long time."""
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 1 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 2 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    return "Done"

slow_thing = RunnableLambda(slow_thing)

async for event in slow_thing.astream_events("some_input", version="v2"):
    print(event)

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Any
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
version	The version of the schema to use either 'v2' or 'v1'. Users should use 'v2'. 'v1' is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in 'v2'. TYPE: Literal['v1', 'v2'] DEFAULT: 'v2'
include_names	Only include events from Runnable objects with matching names. TYPE: Sequence[str] | None DEFAULT: None
include_types	Only include events from Runnable objects with matching types. TYPE: Sequence[str] | None DEFAULT: None
include_tags	Only include events from Runnable objects with matching tags. TYPE: Sequence[str] | None DEFAULT: None
exclude_names	Exclude events from Runnable objects with matching names. TYPE: Sequence[str] | None DEFAULT: None
exclude_types	Exclude events from Runnable objects with matching types. TYPE: Sequence[str] | None DEFAULT: None
exclude_tags	Exclude events from Runnable objects with matching tags. TYPE: Sequence[str] | None DEFAULT: None
**kwargs	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. TYPE: Any DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[StreamEvent]	An async stream of StreamEvent.

RAISES	DESCRIPTION
NotImplementedError	If the version is not 'v1' or 'v2'.

transform

transform(
    input: Iterator[Input], config: RunnableConfig | None = None, **kwargs: Any | None
) -> Iterator[Output]

Transform inputs to outputs.

Default implementation of transform, which buffers input and calls astream.

Subclasses must override this method if they can start producing output while input is still being generated.

PARAMETER	DESCRIPTION
input	An iterator of inputs to the Runnable. TYPE: Iterator[Input]
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
Output	The output of the Runnable.

atransform async

atransform(
    input: AsyncIterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None,
) -> AsyncIterator[Output]

Transform inputs to outputs.

Default implementation of atransform, which buffers input and calls astream.

Subclasses must override this method if they can start producing output while input is still being generated.

PARAMETER	DESCRIPTION
input	An async iterator of inputs to the Runnable. TYPE: AsyncIterator[Input]
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[Output]	The output of the Runnable.

bind

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.

PARAMETER	DESCRIPTION
**kwargs	The arguments to bind to the Runnable. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

model = ChatOllama(model="llama3.1")

# Without bind
chain = model | StrOutputParser()

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

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

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

with_config

with_config(
    config: RunnableConfig | None = None, **kwargs: Any
) -> Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

PARAMETER	DESCRIPTION
config	The config to bind to the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the config bound.

with_listeners

with_listeners(
    *,
    on_start: Callable[[Run], None]
    | Callable[[Run, RunnableConfig], None]
    | None = None,
    on_end: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None,
    on_error: Callable[[Run], None]
    | Callable[[Run, RunnableConfig], None]
    | None = None,
) -> Runnable[Input, Output]

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

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.

PARAMETER	DESCRIPTION
on_start	Called before the Runnable starts running, with the Run object. TYPE: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None DEFAULT: None
on_end	Called after the Runnable finishes running, with the Run object. TYPE: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None DEFAULT: None
on_error	Called if the Runnable throws an error, with the Run object. TYPE: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

import time


def test_runnable(time_to_sleep: int):
    time.sleep(time_to_sleep)


def fn_start(run_obj: Run):
    print("start_time:", run_obj.start_time)


def fn_end(run_obj: Run):
    print("end_time:", run_obj.end_time)


chain = RunnableLambda(test_runnable).with_listeners(
    on_start=fn_start, on_end=fn_end
)
chain.invoke(2)

with_alisteners

with_alisteners(
    *,
    on_start: AsyncListener | None = None,
    on_end: AsyncListener | None = None,
    on_error: AsyncListener | None = None,
) -> Runnable[Input, Output]

Bind async lifecycle listeners to a Runnable.

Returns a new Runnable.

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.

PARAMETER	DESCRIPTION
on_start	Called asynchronously before the Runnable starts running, with the Run object. TYPE: AsyncListener | None DEFAULT: None
on_end	Called asynchronously after the Runnable finishes running, with the Run object. TYPE: AsyncListener | None DEFAULT: None
on_error	Called asynchronously if the Runnable throws an error, with the Run object. TYPE: AsyncListener | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio


def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()


async def test_runnable(time_to_sleep: int):
    print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
    await asyncio.sleep(time_to_sleep)
    print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")


async def fn_start(run_obj: Runnable):
    print(f"on start callback starts at {format_t(time.time())}")
    await asyncio.sleep(3)
    print(f"on start callback ends at {format_t(time.time())}")


async def fn_end(run_obj: Runnable):
    print(f"on end callback starts at {format_t(time.time())}")
    await asyncio.sleep(2)
    print(f"on end callback ends at {format_t(time.time())}")


runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start, on_end=fn_end
)


async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))


asyncio.run(concurrent_runs())
# Result:
# on start callback starts at 2025-03-01T07:05:22.875378+00:00
# on start callback starts at 2025-03-01T07:05:22.875495+00:00
# on start callback ends at 2025-03-01T07:05:25.878862+00:00
# on start callback ends at 2025-03-01T07:05:25.878947+00:00
# Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
# Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
# Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
# on end callback starts at 2025-03-01T07:05:27.882360+00:00
# Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
# on end callback starts at 2025-03-01T07:05:28.882428+00:00
# on end callback ends at 2025-03-01T07:05:29.883893+00:00
# on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_types

with_types(
    *, input_type: type[Input] | None = None, output_type: type[Output] | None = None
) -> Runnable[Input, Output]

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

PARAMETER	DESCRIPTION
input_type	The input type to bind to the Runnable. TYPE: type[Input] | None DEFAULT: None
output_type	The output type to bind to the Runnable. TYPE: type[Output] | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the types bound.

with_retry

with_retry(
    *,
    retry_if_exception_type: tuple[type[BaseException], ...] = (Exception,),
    wait_exponential_jitter: bool = True,
    exponential_jitter_params: ExponentialJitterParams | None = None,
    stop_after_attempt: int = 3,
) -> Runnable[Input, Output]

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

PARAMETER	DESCRIPTION
retry_if_exception_type	A tuple of exception types to retry on. TYPE: tuple[type[BaseException], ...] DEFAULT: (Exception,)
wait_exponential_jitter	Whether to add jitter to the wait time between retries. TYPE: bool DEFAULT: True
stop_after_attempt	The maximum number of attempts to make before giving up. TYPE: int DEFAULT: 3
exponential_jitter_params	Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values). TYPE: ExponentialJitterParams | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable that retries the original Runnable on exceptions.

Example

from langchain_core.runnables import RunnableLambda

count = 0


def _lambda(x: int) -> None:
    global count
    count = count + 1
    if x == 1:
        raise ValueError("x is 1")
    else:
        pass


runnable = RunnableLambda(_lambda)
try:
    runnable.with_retry(
        stop_after_attempt=2,
        retry_if_exception_type=(ValueError,),
    ).invoke(1)
except ValueError:
    pass

assert count == 2

map

map() -> Runnable[list[Input], list[Output]]

Return a new Runnable that maps a list of inputs to a list of outputs.

Calls invoke with each input.

RETURNS	DESCRIPTION
Runnable[list[Input], list[Output]]	A new Runnable that maps a list of inputs to a list of outputs.

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]

with_fallbacks

with_fallbacks(
    fallbacks: Sequence[Runnable[Input, Output]],
    *,
    exceptions_to_handle: tuple[type[BaseException], ...] = (Exception,),
    exception_key: str | None = None,
) -> RunnableWithFallbacks[Input, Output]

Add fallbacks to a Runnable, returning a new Runnable.

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

PARAMETER	DESCRIPTION
fallbacks	A sequence of runnables to try if the original Runnable fails. TYPE: Sequence[Runnable[Input, Output]]
exceptions_to_handle	A tuple of exception types to handle. TYPE: tuple[type[BaseException], ...] DEFAULT: (Exception,)
exception_key	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. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each Fallback in order, upon failures.

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

PARAMETER	DESCRIPTION
fallbacks	A sequence of runnables to try if the original Runnable fails. TYPE: Sequence[Runnable[Input, Output]]
exceptions_to_handle	A tuple of exception types to handle. TYPE: tuple[type[BaseException], ...] DEFAULT: (Exception,)
exception_key	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. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each Fallback in order, upon failures.

as_tool

as_tool(
    args_schema: type[BaseModel] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    arg_types: dict[str, type] | None = None,
) -> BaseTool

Create a BaseTool from a Runnable.

as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Where possible, schemas are inferred from runnable.get_input_schema.

Alternatively (e.g., if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema.

You can also pass arg_types to just specify the required arguments and their types.

PARAMETER	DESCRIPTION
args_schema	The schema for the tool. TYPE: type[BaseModel] | None DEFAULT: None
name	The name of the tool. TYPE: str | None DEFAULT: None
description	The description of the tool. TYPE: str | None DEFAULT: None
arg_types	A dictionary of argument names to types. TYPE: dict[str, type] | None DEFAULT: None

RETURNS	DESCRIPTION
BaseTool	A BaseTool instance.

Typed dict input:

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda


class Args(TypedDict):
    a: int
    b: list[int]


def f(x: Args) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via args_schema:

from typing import Any
from pydantic import BaseModel, Field
from langchain_core.runnables import RunnableLambda

def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

class FSchema(BaseModel):
    """Apply a function to an integer and list of integers."""

    a: int = Field(..., description="Integer")
    b: list[int] = Field(..., description="List of ints")

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via arg_types:

from typing import Any
from langchain_core.runnables import RunnableLambda


def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})

str input:

from langchain_core.runnables import RunnableLambda


def f(x: str) -> str:
    return x + "a"


def g(x: str) -> str:
    return x + "z"


runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")

__init__

__init__(*args: Any, **kwargs: Any) -> None

is_lc_serializable classmethod

is_lc_serializable() -> bool

Is this class serializable?

By design, even if a class inherits from Serializable, it is not serializable by default. This is to prevent accidental serialization of objects that should not be serialized.

RETURNS	DESCRIPTION
bool	Whether the class is serializable. Default is False.

get_lc_namespace classmethod

get_lc_namespace() -> list[str]

Get the namespace of the LangChain object.

For example, if the class is langchain.llms.openai.OpenAI, then the namespace is ["langchain", "llms", "openai"]

RETURNS	DESCRIPTION
list[str]	The namespace.

lc_id classmethod

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object.

For example, for the class langchain.llms.openai.OpenAI, the id is ["langchain", "llms", "openai", "OpenAI"].

to_json

to_json() -> SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

RETURNS	DESCRIPTION
SerializedConstructor | SerializedNotImplemented	A JSON-serializable representation of the Runnable.

to_json_not_implemented

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

RETURNS	DESCRIPTION
SerializedNotImplemented	SerializedNotImplemented.

validate_environment

validate_environment() -> Self

Validate that api key and python package exists in environment.

configurable_fields

configurable_fields(
    **kwargs: AnyConfigurableField,
) -> RunnableSerializable[Input, Output]

Configure particular Runnable fields at runtime.

PARAMETER	DESCRIPTION
**kwargs	A dictionary of ConfigurableField instances to configure. TYPE: AnyConfigurableField DEFAULT: {}

RAISES	DESCRIPTION
ValueError	If a configuration key is not found in the Runnable.

RETURNS	DESCRIPTION
RunnableSerializable[Input, Output]	A new Runnable with the fields configured.

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print("max_tokens_20: ", model.invoke("tell me something about chess").content)

# max_tokens = 200
print(
    "max_tokens_200: ",
    model.with_config(configurable={"output_token_number": 200})
    .invoke("tell me something about chess")
    .content,
)

configurable_alternatives

configurable_alternatives(
    which: ConfigurableField,
    *,
    default_key: str = "default",
    prefix_keys: bool = False,
    **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]],
) -> RunnableSerializable[Input, Output]

Configure alternatives for Runnable objects that can be set at runtime.

PARAMETER	DESCRIPTION
which	The ConfigurableField instance that will be used to select the alternative. TYPE: ConfigurableField
default_key	The default key to use if no alternative is selected. TYPE: str DEFAULT: 'default'
prefix_keys	Whether to prefix the keys with the ConfigurableField id. TYPE: bool DEFAULT: False
**kwargs	A dictionary of keys to Runnable instances or callables that return Runnable instances. TYPE: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]] DEFAULT: {}

RETURNS	DESCRIPTION
RunnableSerializable[Input, Output]	A new Runnable with the alternatives configured.

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-sonnet-4-5-20250929"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI(),
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

# uses ChatOpenAI
print(
    model.with_config(configurable={"llm": "openai"})
    .invoke("which organization created you?")
    .content
)

set_verbose

set_verbose(verbose: bool | None) -> bool

If verbose is None, set it.

This allows users to pass in None as verbose to access the global setting.

PARAMETER	DESCRIPTION
verbose	The verbosity setting to use. TYPE: bool | None

RETURNS	DESCRIPTION
bool	The verbosity setting to use.

generate_prompt

generate_prompt(
    prompts: list[PromptValue],
    stop: list[str] | None = None,
    callbacks: Callbacks = None,
    **kwargs: Any,
) -> LLMResult

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

1. Take advantage of batched calls,
2. Need more output from the model than just the top generated value,
3. Are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models).

PARAMETER	DESCRIPTION
prompts	List of PromptValue objects. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessage objects for chat models). TYPE: list[PromptValue]
stop	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: list[str] | None DEFAULT: None
callbacks	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: Callbacks DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
LLMResult	An LLMResult, which contains a list of candidate Generation objects for each input prompt and additional model provider-specific output.

agenerate_prompt async

agenerate_prompt(
    prompts: list[PromptValue],
    stop: list[str] | None = None,
    callbacks: Callbacks = None,
    **kwargs: Any,
) -> LLMResult

Asynchronously pass a sequence of prompts and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

1. Take advantage of batched calls,
2. Need more output from the model than just the top generated value,
3. Are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models).

PARAMETER	DESCRIPTION
prompts	List of PromptValue objects. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessage objects for chat models). TYPE: list[PromptValue]
stop	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: list[str] | None DEFAULT: None
callbacks	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: Callbacks DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
LLMResult	An LLMResult, which contains a list of candidate Generation objects for each input prompt and additional model provider-specific output.

get_token_ids

get_token_ids(text: str) -> list[int]

Return the ordered IDs of the tokens in a text.

PARAMETER	DESCRIPTION
text	The string input to tokenize. TYPE: str

RETURNS	DESCRIPTION
list[int]	A list of IDs corresponding to the tokens in the text, in order they occur in the text.

get_num_tokens_from_messages

get_num_tokens_from_messages(
    messages: list[BaseMessage], tools: Sequence | None = None
) -> int

Get the number of tokens in the messages.

Useful for checking if an input fits in a model's context window.

Note

The base implementation of get_num_tokens_from_messages ignores tool schemas.

PARAMETER	DESCRIPTION
messages	The message inputs to tokenize. TYPE: list[BaseMessage]
tools	If provided, sequence of dict, BaseModel, function, or BaseTool objects to be converted to tool schemas. TYPE: Sequence | None DEFAULT: None

RETURNS	DESCRIPTION
int	The sum of the number of tokens across the messages.

generate

generate(
    messages: list[list[BaseMessage]],
    stop: list[str] | None = None,
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = None,
    **kwargs: Any,
) -> LLMResult

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

1. Take advantage of batched calls,
2. Need more output from the model than just the top generated value,
3. Are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models).

PARAMETER	DESCRIPTION
messages	List of list of messages. TYPE: list[list[BaseMessage]]
stop	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: list[str] | None DEFAULT: None
callbacks	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: Callbacks DEFAULT: None
tags	The tags to apply. TYPE: list[str] | None DEFAULT: None
metadata	The metadata to apply. TYPE: dict[str, Any] | None DEFAULT: None
run_name	The name of the run. TYPE: str | None DEFAULT: None
run_id	The ID of the run. TYPE: UUID | None DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
LLMResult	An LLMResult, which contains a list of candidate Generations for each input prompt and additional model provider-specific output.

agenerate async

agenerate(
    messages: list[list[BaseMessage]],
    stop: list[str] | None = None,
    callbacks: Callbacks = None,
    *,
    tags: list[str] | None = None,
    metadata: dict[str, Any] | None = None,
    run_name: str | None = None,
    run_id: UUID | None = None,
    **kwargs: Any,
) -> LLMResult

Asynchronously pass a sequence of prompts to a model and return generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

1. Take advantage of batched calls,
2. Need more output from the model than just the top generated value,
3. Are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models).

PARAMETER	DESCRIPTION
messages	List of list of messages. TYPE: list[list[BaseMessage]]
stop	Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. TYPE: list[str] | None DEFAULT: None
callbacks	Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. TYPE: Callbacks DEFAULT: None
tags	The tags to apply. TYPE: list[str] | None DEFAULT: None
metadata	The metadata to apply. TYPE: dict[str, Any] | None DEFAULT: None
run_name	The name of the run. TYPE: str | None DEFAULT: None
run_id	The ID of the run. TYPE: UUID | None DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
LLMResult	An LLMResult, which contains a list of candidate Generations for each input prompt and additional model provider-specific output.

dict

dict(**kwargs: Any) -> dict

Return a dictionary of the LLM.

bind_tools

bind_tools(
    tools: Sequence[dict[str, Any] | Type[BaseModel] | Callable | BaseTool],
    **kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]

Bind tools to the model.

PARAMETER	DESCRIPTION
tools	Sequence of tools to bind to the model. TYPE: Sequence[Dict[str, Any] | type | Callable | BaseTool]
tool_choice	The tool to use. If "any" then any tool can be used. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[LanguageModelInput, AIMessage]	A Runnable that returns a message.

with_structured_output

with_structured_output(
    schema: dict | Type[BaseModel],
    method: Literal[
        "function_calling", "tool_calling", "json_mode", "json_schema"
    ] = "json_schema",
    **kwargs: Any,
) -> Runnable[LanguageModelInput, dict | BaseModel]

Model wrapper that returns outputs formatted to match the given schema.

Given schema can be a Pydantic class or a dict.

PARAMETER	DESCRIPTION
schema	The output schema as a dict or a Pydantic class. If a Pydantic class then the model output will be an object of that class. If a dict then the model output will be a dict. TYPE: dict | Type[BaseModel]
method	The method for steering model generation, one of: 'function_calling' or 'tool_calling': Uses Cohere's tool-calling (formerly called function calling) API: https://docs.cohere.com/v2/docs/tool-use 'json_schema': Uses Cohere's Structured Output API: https://docs.cohere.com/docs/structured-outputs Allows the user to pass a json schema (or pydantic) to the model for structured output. This is the default method. Supported for 'command-r', 'command-r-plus', and later models. - 'json_mode': Uses Cohere's Structured Output API: https://docs.cohere.com/docs/structured-outputs Supported for 'command-r', 'command-r-plus', and later models. TYPE: Literal['function_calling', 'tool_calling', 'json_mode', 'json_schema'] DEFAULT: 'json_schema'

RETURNS	DESCRIPTION
Runnable[LanguageModelInput, dict | BaseModel]	A Runnable that takes any ChatModel input and returns either a dict or Pydantic class as output.

get_num_tokens

get_num_tokens(text: str) -> int

Calculate number of tokens.

CohereCitation dataclass

Cohere has fine-grained citations that specify the exact part of text.

More info at https://docs.cohere.com/docs/documents-and-citations

start instance-attribute

start: int

The index of text that the citation starts at, counting from zero.

For example, a generation of 'Hello, world!' with a citation on 'world' would have a start value of 7. This is because the citation starts at 'w', which is the seventh character.

end instance-attribute

end: int

The index of text that the citation ends after, counting from zero.

For example, a generation of 'Hello, world!' with a citation on 'world' would have an end value of 11. This is because the citation ends after 'd', which is the eleventh character.

text instance-attribute

text: str

The text of the citation.

For example, a generation of 'Hello, world!' with a citation of 'world' would have a text value of 'world'.

documents instance-attribute

documents: list[Mapping[str, Any]]

The contents of the documents that were cited.

When used with agents these will be the contents of relevant agent outputs.

Every document will have a string field called id. This can be used with the document_ids field to deduplicate documents across several citations. The id field is created on the document when it doesn't already exist.

document_ids instance-attribute

document_ids: set[str]

A set of the id field from all the documents in the documents field.

CohereEmbeddings

Bases: BaseModel, Embeddings

Implements the Embeddings interface with Cohere's text representation language models.

Find out more about us at https://cohere.com and https://huggingface.co/CohereForAI

This implementation uses the Embed API - see https://docs.cohere.com/reference/embed

To use this you'll need to a Cohere API key - either pass it to cohere_api_key parameter or set the COHERE_API_KEY environment variable.

API keys are available on https://cohere.com - it's free to sign up and trial API keys work with this implementation.

Basic Example

cohere_embeddings = CohereEmbeddings(model="embed-english-light-v3.0")
text = "This is a test document."

query_result = cohere_embeddings.embed_query(text)
print(query_result)

doc_result = cohere_embeddings.embed_documents([text])
print(doc_result)

METHOD	DESCRIPTION
validate_environment	Validate that api key and python package exists in environment.
validate_model_specified	Validate that model is specified.
embed_with_retry	Use tenacity to retry the embed call.
aembed_with_retry	Use tenacity to retry the embed call.
embed_documents	Embed a list of document texts.
aembed_documents	Async call out to Cohere's embedding endpoint.
embed_query	Call out to Cohere's embedding endpoint.
aembed_query	Async call out to Cohere's embedding endpoint.

client instance-attribute

client: Any

Cohere client.

async_client instance-attribute

async_client: Any

Cohere async client.

model class-attribute instance-attribute

model: str | None = None

Model name to use. It is mandatory to specify the model name.

truncate class-attribute instance-attribute

truncate: str | None = None

Truncate embeddings that are too long from start or end ("NONE"|"START"|"END")

embedding_types class-attribute instance-attribute

embedding_types: Sequence[str] = ['float']

Specifies the types of embeddings you want to get back

max_retries class-attribute instance-attribute

max_retries: int = 3

Maximum number of retries to make when generating.

request_timeout class-attribute instance-attribute

request_timeout: float | None = None

Timeout in seconds for the Cohere API request.

user_agent class-attribute instance-attribute

user_agent: str = 'langchain:partner'

Identifier for the application making the request.

base_url class-attribute instance-attribute

base_url: str | None = None

Override the default Cohere API URL.

validate_environment classmethod

validate_environment(values: dict) -> Any

Validate that api key and python package exists in environment.

validate_model_specified

validate_model_specified() -> Self

Validate that model is specified.

embed_with_retry

embed_with_retry(**kwargs: Any) -> Any

Use tenacity to retry the embed call.

aembed_with_retry

aembed_with_retry(**kwargs: Any) -> Any

Use tenacity to retry the embed call.

embed_documents

embed_documents(texts: list[str]) -> list[list[float]]

Embed a list of document texts.

PARAMETER	DESCRIPTION
texts	The list of texts to embed. TYPE: list[str]

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings, one for each text.

aembed_documents async

aembed_documents(texts: list[str]) -> list[list[float]]

Async call out to Cohere's embedding endpoint.

PARAMETER	DESCRIPTION
texts	The list of texts to embed. TYPE: list[str]

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings, one for each text.

embed_query

embed_query(text: str) -> list[float]

Call out to Cohere's embedding endpoint.

PARAMETER	DESCRIPTION
text	The text to embed. TYPE: str

RETURNS	DESCRIPTION
list[float]	Embeddings for the text.

aembed_query async

aembed_query(text: str) -> list[float]

Async call out to Cohere's embedding endpoint.

PARAMETER	DESCRIPTION
text	The text to embed. TYPE: str

RETURNS	DESCRIPTION
list[float]	Embeddings for the text.

CohereRagRetriever

Bases: BaseRetriever

Cohere Chat API with RAG.

METHOD	DESCRIPTION
get_name	Get the name of the Runnable.
get_input_schema	Get a Pydantic model that can be used to validate input to the Runnable.
get_input_jsonschema	Get a JSON schema that represents the input to the Runnable.
get_output_schema	Get a Pydantic model that can be used to validate output to the Runnable.
get_output_jsonschema	Get a JSON schema that represents the output of the Runnable.
config_schema	The type of config this Runnable accepts specified as a Pydantic model.
get_config_jsonschema	Get a JSON schema that represents the config of the Runnable.
get_graph	Return a graph representation of this Runnable.
get_prompts	Return a list of prompts used by this Runnable.
__or__	Runnable "or" operator.
__ror__	Runnable "reverse-or" operator.
pipe	Pipe Runnable objects.
pick	Pick keys from the output dict of this Runnable.
assign	Assigns new fields to the dict output of this Runnable.
invoke	Invoke the retriever to get relevant documents.
ainvoke	Asynchronously invoke the retriever to get relevant documents.
batch	Default implementation runs invoke in parallel using a thread pool executor.
batch_as_completed	Run invoke in parallel on a list of inputs.
abatch	Default implementation runs ainvoke in parallel using asyncio.gather.
abatch_as_completed	Run ainvoke in parallel on a list of inputs.
stream	Default implementation of stream, which calls invoke.
astream	Default implementation of astream, which calls ainvoke.
astream_log	Stream all output from a Runnable, as reported to the callback system.
astream_events	Generate a stream of events.
transform	Transform inputs to outputs.
atransform	Transform inputs to outputs.
bind	Bind arguments to a Runnable, returning a new Runnable.
with_config	Bind config to a Runnable, returning a new Runnable.
with_listeners	Bind lifecycle listeners to a Runnable, returning a new Runnable.
with_alisteners	Bind async lifecycle listeners to a Runnable.
with_types	Bind input and output types to a Runnable, returning a new Runnable.
with_retry	Create a new Runnable that retries the original Runnable on exceptions.
map	Return a new Runnable that maps a list of inputs to a list of outputs.
with_fallbacks	Add fallbacks to a Runnable, returning a new Runnable.
as_tool	Create a BaseTool from a Runnable.
__init__
is_lc_serializable	Is this class serializable?
get_lc_namespace	Get the namespace of the LangChain object.
lc_id	Return a unique identifier for this class for serialization purposes.
to_json	Serialize the Runnable to JSON.
to_json_not_implemented	Serialize a "not implemented" object.
configurable_fields	Configure particular Runnable fields at runtime.
configurable_alternatives	Configure alternatives for Runnable objects that can be set at runtime.

name class-attribute instance-attribute

name: str | None = None

The name of the Runnable. Used for debugging and tracing.

InputType property

InputType: type[Input]

Input type.

The type of input this Runnable accepts specified as a type annotation.

RAISES	DESCRIPTION
TypeError	If the input type cannot be inferred.

OutputType property

OutputType: type[Output]

Output Type.

The type of output this Runnable produces specified as a type annotation.

RAISES	DESCRIPTION
TypeError	If the output type cannot be inferred.

input_schema property

input_schema: type[BaseModel]

The type of input this Runnable accepts specified as a Pydantic model.

output_schema property

output_schema: type[BaseModel]

Output schema.

The type of output this Runnable produces specified as a Pydantic model.

config_specs property

config_specs: list[ConfigurableFieldSpec]

List configurable fields for this Runnable.

lc_secrets property

lc_secrets: dict[str, str]

A map of constructor argument names to secret ids.

For example, {"openai_api_key": "OPENAI_API_KEY"}

lc_attributes property

lc_attributes: dict

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor.

Default is an empty dictionary.

tags class-attribute instance-attribute

tags: list[str] | None = None

Optional list of tags associated with the retriever.

These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks.

You can use these to eg identify a specific instance of a retriever with its use case.

metadata class-attribute instance-attribute

metadata: dict[str, Any] | None = None

Optional metadata associated with the retriever.

This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks.

You can use these to eg identify a specific instance of a retriever with its use case.

llm instance-attribute

llm: BaseChatModel

Cohere ChatModel to use.

get_name

get_name(suffix: str | None = None, *, name: str | None = None) -> str

Get the name of the Runnable.

PARAMETER	DESCRIPTION
suffix	An optional suffix to append to the name. TYPE: str | None DEFAULT: None
name	An optional name to use instead of the Runnable's name. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
str	The name of the Runnable.

get_input_schema

get_input_schema(config: RunnableConfig | None = None) -> type[BaseModel]

Get a Pydantic model that can be used to validate input to the Runnable.

Runnable objects that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the Runnable is invoked with.

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

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
type[BaseModel]	A Pydantic model that can be used to validate input.

get_input_jsonschema

get_input_jsonschema(config: RunnableConfig | None = None) -> dict[str, Any]

Get a JSON schema that represents the input to the Runnable.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
dict[str, Any]	A JSON schema that represents the input to the Runnable.

Example

from langchain_core.runnables import RunnableLambda


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


runnable = RunnableLambda(add_one)

print(runnable.get_input_jsonschema())

Added in langchain-core 0.3.0

get_output_schema

get_output_schema(config: RunnableConfig | None = None) -> type[BaseModel]

Get a Pydantic model that can be used to validate output to the Runnable.

Runnable objects 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.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
type[BaseModel]	A Pydantic model that can be used to validate output.

get_output_jsonschema

get_output_jsonschema(config: RunnableConfig | None = None) -> dict[str, Any]

Get a JSON schema that represents the output of the Runnable.

PARAMETER	DESCRIPTION
config	A config to use when generating the schema. TYPE: RunnableConfig | None DEFAULT: None

RETURNS	DESCRIPTION
dict[str, Any]	A JSON schema that represents the output of the Runnable.

Example

from langchain_core.runnables import RunnableLambda


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


runnable = RunnableLambda(add_one)

print(runnable.get_output_jsonschema())

Added in langchain-core 0.3.0

config_schema

config_schema(*, include: Sequence[str] | None = 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.

PARAMETER	DESCRIPTION
include	A list of fields to include in the config schema. TYPE: Sequence[str] | None DEFAULT: None

RETURNS	DESCRIPTION
type[BaseModel]	A Pydantic model that can be used to validate config.

get_config_jsonschema

get_config_jsonschema(*, include: Sequence[str] | None = None) -> dict[str, Any]

Get a JSON schema that represents the config of the Runnable.

PARAMETER	DESCRIPTION
include	A list of fields to include in the config schema. TYPE: Sequence[str] | None DEFAULT: None

RETURNS	DESCRIPTION
dict[str, Any]	A JSON schema that represents the config of the Runnable.

Added in langchain-core 0.3.0

get_graph

get_graph(config: RunnableConfig | None = None) -> Graph

Return a graph representation of this Runnable.

get_prompts

get_prompts(config: RunnableConfig | None = None) -> list[BasePromptTemplate]

Return a list of prompts used by this Runnable.

__or__

__or__(
    other: Runnable[Any, Other]
    | Callable[[Iterator[Any]], Iterator[Other]]
    | Callable[[AsyncIterator[Any]], AsyncIterator[Other]]
    | Callable[[Any], Other]
    | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any],
) -> RunnableSerializable[Input, Other]

Runnable "or" operator.

Compose this Runnable with another object to create a RunnableSequence.

PARAMETER	DESCRIPTION
other	Another Runnable or a Runnable-like object. TYPE: Runnable[Any, Other] | Callable[[Iterator[Any]], Iterator[Other]] | Callable[[AsyncIterator[Any]], AsyncIterator[Other]] | Callable[[Any], Other] | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any]

RETURNS	DESCRIPTION
RunnableSerializable[Input, Other]	A new Runnable.

__ror__

__ror__(
    other: Runnable[Other, Any]
    | Callable[[Iterator[Other]], Iterator[Any]]
    | Callable[[AsyncIterator[Other]], AsyncIterator[Any]]
    | Callable[[Other], Any]
    | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any],
) -> RunnableSerializable[Other, Output]

Runnable "reverse-or" operator.

Compose this Runnable with another object to create a RunnableSequence.

PARAMETER	DESCRIPTION
other	Another Runnable or a Runnable-like object. TYPE: Runnable[Other, Any] | Callable[[Iterator[Other]], Iterator[Any]] | Callable[[AsyncIterator[Other]], AsyncIterator[Any]] | Callable[[Other], Any] | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any]

RETURNS	DESCRIPTION
RunnableSerializable[Other, Output]	A new Runnable.

pipe

pipe(
    *others: Runnable[Any, Other] | Callable[[Any], Other], name: str | None = None
) -> RunnableSerializable[Input, Other]

Pipe Runnable objects.

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]

PARAMETER	DESCRIPTION
*others	Other Runnable or Runnable-like objects to compose TYPE: Runnable[Any, Other] | Callable[[Any], Other] DEFAULT: ()
name	An optional name for the resulting RunnableSequence. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
RunnableSerializable[Input, Other]	A new Runnable.

pick

pick(keys: str | list[str]) -> RunnableSerializable[Any, Any]

Pick keys from the output dict of this Runnable.

Pick a 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 a 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]"}

PARAMETER	DESCRIPTION
keys	A key or list of keys to pick from the output dict. TYPE: str | list[str]

RETURNS	DESCRIPTION
RunnableSerializable[Any, Any]	a new Runnable.

assign

assign(
    **kwargs: Runnable[dict[str, Any], Any]
    | Callable[[dict[str, Any]], Any]
    | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]],
) -> RunnableSerializable[Any, Any]

Assigns new fields to the dict output of this Runnable.

from langchain_core.language_models.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}"
)
model = FakeStreamingListLLM(responses=["foo-lish"])

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

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

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

PARAMETER	DESCRIPTION
**kwargs	A mapping of keys to Runnable or Runnable-like objects that will be invoked with the entire output dict of this Runnable. TYPE: Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any] | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]] DEFAULT: {}

RETURNS	DESCRIPTION
RunnableSerializable[Any, Any]	A new Runnable.

invoke

invoke(
    input: str, config: RunnableConfig | None = None, **kwargs: Any
) -> list[Document]

Invoke the retriever to get relevant documents.

Main entry point for synchronous retriever invocations.

PARAMETER	DESCRIPTION
input	The query string. TYPE: str
config	Configuration for the retriever. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional arguments to pass to the retriever. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
list[Document]	List of relevant documents.

Examples:

retriever.invoke("query")

ainvoke async

ainvoke(
    input: str, config: RunnableConfig | None = None, **kwargs: Any
) -> list[Document]

Asynchronously invoke the retriever to get relevant documents.

Main entry point for asynchronous retriever invocations.

PARAMETER	DESCRIPTION
input	The query string. TYPE: str
config	Configuration for the retriever. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional arguments to pass to the retriever. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
list[Document]	List of relevant documents.

Examples:

await retriever.ainvoke("query")

batch

batch(
    inputs: list[Input],
    config: RunnableConfig | list[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> 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 must override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: list[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | list[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

RETURNS	DESCRIPTION
list[Output]	A list of outputs from the Runnable.

batch_as_completed

batch_as_completed(
    inputs: Sequence[Input],
    config: RunnableConfig | Sequence[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> Iterator[tuple[int, Output | Exception]]

Run invoke in parallel on a list of inputs.

Yields results as they complete.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: Sequence[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | Sequence[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
tuple[int, Output | Exception]	Tuples of the index of the input and the output from the Runnable.

abatch async

abatch(
    inputs: list[Input],
    config: RunnableConfig | list[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> list[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

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

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

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: list[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | list[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

RETURNS	DESCRIPTION
list[Output]	A list of outputs from the Runnable.

abatch_as_completed async

abatch_as_completed(
    inputs: Sequence[Input],
    config: RunnableConfig | Sequence[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any | None,
) -> AsyncIterator[tuple[int, Output | Exception]]

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

PARAMETER	DESCRIPTION
inputs	A list of inputs to the Runnable. TYPE: Sequence[Input]
config	A config to use when invoking the Runnable. The config supports standard keys like 'tags', 'metadata' for tracing purposes, 'max_concurrency' for controlling how much work to do in parallel, and other keys. Please refer to RunnableConfig for more details. TYPE: RunnableConfig | Sequence[RunnableConfig] | None DEFAULT: None
return_exceptions	Whether to return exceptions instead of raising them. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[tuple[int, Output | Exception]]	A tuple of the index of the input and the output from the Runnable.

stream

stream(
    input: Input, config: RunnableConfig | None = None, **kwargs: Any | None
) -> Iterator[Output]

Default implementation of stream, which calls invoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
Output	The output of the Runnable.

astream async

astream(
    input: Input, config: RunnableConfig | None = None, **kwargs: Any | None
) -> AsyncIterator[Output]

Default implementation of astream, which calls ainvoke.

Subclasses must override this method if they support streaming output.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Input
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[Output]	The output of the Runnable.

astream_log async

astream_log(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    diff: bool = True,
    with_streamed_output_list: bool = True,
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any,
) -> 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.

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Any
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
diff	Whether to yield diffs between each step or the current state. TYPE: bool DEFAULT: True
with_streamed_output_list	Whether to yield the streamed_output list. TYPE: bool DEFAULT: True
include_names	Only include logs with these names. TYPE: Sequence[str] | None DEFAULT: None
include_types	Only include logs with these types. TYPE: Sequence[str] | None DEFAULT: None
include_tags	Only include logs with these tags. TYPE: Sequence[str] | None DEFAULT: None
exclude_names	Exclude logs with these names. TYPE: Sequence[str] | None DEFAULT: None
exclude_types	Exclude logs with these types. TYPE: Sequence[str] | None DEFAULT: None
exclude_tags	Exclude logs with these tags. TYPE: Sequence[str] | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]	A RunLogPatch or RunLog object.

astream_events async

astream_events(
    input: Any,
    config: RunnableConfig | None = None,
    *,
    version: Literal["v1", "v2"] = "v2",
    include_names: Sequence[str] | None = None,
    include_types: Sequence[str] | None = None,
    include_tags: Sequence[str] | None = None,
    exclude_names: Sequence[str] | None = None,
    exclude_types: Sequence[str] | None = None,
    exclude_tags: Sequence[str] | None = None,
    **kwargs: Any,
) -> AsyncIterator[StreamEvent]

Generate a stream of events.

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

A StreamEvent is a dictionary with the following schema:

• event: Event names are of the format: on_[runnable_type]_(start|stream|end).
• name: The name of the Runnable that generated the event.
• run_id: 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.
• parent_ids: The IDs of the parent runnables that generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
• tags: The tags of the Runnable that generated the event.
• metadata: The metadata of the Runnable that generated the event.
• data: The data associated with the event. The contents of this field depend on the type of event. See the table below for more details.

Below is a table that illustrates some events 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.

Note

This reference table is for the v2 version of the schema.

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]]}	AIMessageChunk(content="hello world")
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_end	'some_tool'			{"x": 1, "y": "2"}
on_retriever_start	'[retriever name]'		{"query": "hello"}
on_retriever_end	'[retriever name]'		{"query": "hello"}	[Document(...), ..]
on_prompt_start	'[template_name]'		{"question": "hello"}
on_prompt_end	'[template_name]'		{"question": "hello"}	ChatPromptValue(messages: [SystemMessage, ...])

In addition to the standard events, users can also dispatch custom events (see example below).

Custom events will be only be surfaced with in the v2 version of the API!

A custom event has following format:

Attribute	Type	Description
name	str	A user defined name for the event.
data	Any	The data associated with the event. This can be anything, though we suggest making it JSON serializable.

Here are declarations associated with the standard 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"]})

For instance:

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="v2")]

# Will produce the following events
# (run_id, and parent_ids 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": [],
    },
]

Example: Dispatch Custom Event

from langchain_core.callbacks.manager import (
    adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio


async def slow_thing(some_input: str, config: RunnableConfig) -> str:
    """Do something that takes a long time."""
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 1 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 2 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    return "Done"

slow_thing = RunnableLambda(slow_thing)

async for event in slow_thing.astream_events("some_input", version="v2"):
    print(event)

PARAMETER	DESCRIPTION
input	The input to the Runnable. TYPE: Any
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
version	The version of the schema to use either 'v2' or 'v1'. Users should use 'v2'. 'v1' is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in 'v2'. TYPE: Literal['v1', 'v2'] DEFAULT: 'v2'
include_names	Only include events from Runnable objects with matching names. TYPE: Sequence[str] | None DEFAULT: None
include_types	Only include events from Runnable objects with matching types. TYPE: Sequence[str] | None DEFAULT: None
include_tags	Only include events from Runnable objects with matching tags. TYPE: Sequence[str] | None DEFAULT: None
exclude_names	Exclude events from Runnable objects with matching names. TYPE: Sequence[str] | None DEFAULT: None
exclude_types	Exclude events from Runnable objects with matching types. TYPE: Sequence[str] | None DEFAULT: None
exclude_tags	Exclude events from Runnable objects with matching tags. TYPE: Sequence[str] | None DEFAULT: None
**kwargs	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. TYPE: Any DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[StreamEvent]	An async stream of StreamEvent.

RAISES	DESCRIPTION
NotImplementedError	If the version is not 'v1' or 'v2'.

transform

transform(
    input: Iterator[Input], config: RunnableConfig | None = None, **kwargs: Any | None
) -> Iterator[Output]

Transform inputs to outputs.

Default implementation of transform, which buffers input and calls astream.

Subclasses must override this method if they can start producing output while input is still being generated.

PARAMETER	DESCRIPTION
input	An iterator of inputs to the Runnable. TYPE: Iterator[Input]
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
Output	The output of the Runnable.

atransform async

atransform(
    input: AsyncIterator[Input],
    config: RunnableConfig | None = None,
    **kwargs: Any | None,
) -> AsyncIterator[Output]

Transform inputs to outputs.

Default implementation of atransform, which buffers input and calls astream.

Subclasses must override this method if they can start producing output while input is still being generated.

PARAMETER	DESCRIPTION
input	An async iterator of inputs to the Runnable. TYPE: AsyncIterator[Input]
config	The config to use for the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any | None DEFAULT: {}

YIELDS	DESCRIPTION
AsyncIterator[Output]	The output of the Runnable.

bind

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.

PARAMETER	DESCRIPTION
**kwargs	The arguments to bind to the Runnable. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the arguments bound.

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

model = ChatOllama(model="llama3.1")

# Without bind
chain = model | StrOutputParser()

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

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

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

with_config

with_config(
    config: RunnableConfig | None = None, **kwargs: Any
) -> Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

PARAMETER	DESCRIPTION
config	The config to bind to the Runnable. TYPE: RunnableConfig | None DEFAULT: None
**kwargs	Additional keyword arguments to pass to the Runnable. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the config bound.

with_listeners

with_listeners(
    *,
    on_start: Callable[[Run], None]
    | Callable[[Run, RunnableConfig], None]
    | None = None,
    on_end: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None,
    on_error: Callable[[Run], None]
    | Callable[[Run, RunnableConfig], None]
    | None = None,
) -> Runnable[Input, Output]

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

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.

PARAMETER	DESCRIPTION
on_start	Called before the Runnable starts running, with the Run object. TYPE: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None DEFAULT: None
on_end	Called after the Runnable finishes running, with the Run object. TYPE: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None DEFAULT: None
on_error	Called if the Runnable throws an error, with the Run object. TYPE: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

import time


def test_runnable(time_to_sleep: int):
    time.sleep(time_to_sleep)


def fn_start(run_obj: Run):
    print("start_time:", run_obj.start_time)


def fn_end(run_obj: Run):
    print("end_time:", run_obj.end_time)


chain = RunnableLambda(test_runnable).with_listeners(
    on_start=fn_start, on_end=fn_end
)
chain.invoke(2)

with_alisteners

with_alisteners(
    *,
    on_start: AsyncListener | None = None,
    on_end: AsyncListener | None = None,
    on_error: AsyncListener | None = None,
) -> Runnable[Input, Output]

Bind async lifecycle listeners to a Runnable.

Returns a new Runnable.

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.

PARAMETER	DESCRIPTION
on_start	Called asynchronously before the Runnable starts running, with the Run object. TYPE: AsyncListener | None DEFAULT: None
on_end	Called asynchronously after the Runnable finishes running, with the Run object. TYPE: AsyncListener | None DEFAULT: None
on_error	Called asynchronously if the Runnable throws an error, with the Run object. TYPE: AsyncListener | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the listeners bound.

Example

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio


def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()


async def test_runnable(time_to_sleep: int):
    print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
    await asyncio.sleep(time_to_sleep)
    print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")


async def fn_start(run_obj: Runnable):
    print(f"on start callback starts at {format_t(time.time())}")
    await asyncio.sleep(3)
    print(f"on start callback ends at {format_t(time.time())}")


async def fn_end(run_obj: Runnable):
    print(f"on end callback starts at {format_t(time.time())}")
    await asyncio.sleep(2)
    print(f"on end callback ends at {format_t(time.time())}")


runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start, on_end=fn_end
)


async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))


asyncio.run(concurrent_runs())
# Result:
# on start callback starts at 2025-03-01T07:05:22.875378+00:00
# on start callback starts at 2025-03-01T07:05:22.875495+00:00
# on start callback ends at 2025-03-01T07:05:25.878862+00:00
# on start callback ends at 2025-03-01T07:05:25.878947+00:00
# Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
# Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
# Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
# on end callback starts at 2025-03-01T07:05:27.882360+00:00
# Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
# on end callback starts at 2025-03-01T07:05:28.882428+00:00
# on end callback ends at 2025-03-01T07:05:29.883893+00:00
# on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_types

with_types(
    *, input_type: type[Input] | None = None, output_type: type[Output] | None = None
) -> Runnable[Input, Output]

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

PARAMETER	DESCRIPTION
input_type	The input type to bind to the Runnable. TYPE: type[Input] | None DEFAULT: None
output_type	The output type to bind to the Runnable. TYPE: type[Output] | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable with the types bound.

with_retry

with_retry(
    *,
    retry_if_exception_type: tuple[type[BaseException], ...] = (Exception,),
    wait_exponential_jitter: bool = True,
    exponential_jitter_params: ExponentialJitterParams | None = None,
    stop_after_attempt: int = 3,
) -> Runnable[Input, Output]

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

PARAMETER	DESCRIPTION
retry_if_exception_type	A tuple of exception types to retry on. TYPE: tuple[type[BaseException], ...] DEFAULT: (Exception,)
wait_exponential_jitter	Whether to add jitter to the wait time between retries. TYPE: bool DEFAULT: True
stop_after_attempt	The maximum number of attempts to make before giving up. TYPE: int DEFAULT: 3
exponential_jitter_params	Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values). TYPE: ExponentialJitterParams | None DEFAULT: None

RETURNS	DESCRIPTION
Runnable[Input, Output]	A new Runnable that retries the original Runnable on exceptions.

Example

from langchain_core.runnables import RunnableLambda

count = 0


def _lambda(x: int) -> None:
    global count
    count = count + 1
    if x == 1:
        raise ValueError("x is 1")
    else:
        pass


runnable = RunnableLambda(_lambda)
try:
    runnable.with_retry(
        stop_after_attempt=2,
        retry_if_exception_type=(ValueError,),
    ).invoke(1)
except ValueError:
    pass

assert count == 2

map

map() -> Runnable[list[Input], list[Output]]

Return a new Runnable that maps a list of inputs to a list of outputs.

Calls invoke with each input.

RETURNS	DESCRIPTION
Runnable[list[Input], list[Output]]	A new Runnable that maps a list of inputs to a list of outputs.

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]

with_fallbacks

with_fallbacks(
    fallbacks: Sequence[Runnable[Input, Output]],
    *,
    exceptions_to_handle: tuple[type[BaseException], ...] = (Exception,),
    exception_key: str | None = None,
) -> RunnableWithFallbacks[Input, Output]

Add fallbacks to a Runnable, returning a new Runnable.

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

PARAMETER	DESCRIPTION
fallbacks	A sequence of runnables to try if the original Runnable fails. TYPE: Sequence[Runnable[Input, Output]]
exceptions_to_handle	A tuple of exception types to handle. TYPE: tuple[type[BaseException], ...] DEFAULT: (Exception,)
exception_key	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. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each Fallback in order, upon failures.

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

PARAMETER	DESCRIPTION
fallbacks	A sequence of runnables to try if the original Runnable fails. TYPE: Sequence[Runnable[Input, Output]]
exceptions_to_handle	A tuple of exception types to handle. TYPE: tuple[type[BaseException], ...] DEFAULT: (Exception,)
exception_key	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. TYPE: str | None DEFAULT: None

RETURNS	DESCRIPTION
RunnableWithFallbacks[Input, Output]	A new Runnable that will try the original Runnable, and then each Fallback in order, upon failures.

as_tool

as_tool(
    args_schema: type[BaseModel] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    arg_types: dict[str, type] | None = None,
) -> BaseTool

Create a BaseTool from a Runnable.

as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Where possible, schemas are inferred from runnable.get_input_schema.

Alternatively (e.g., if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema.

You can also pass arg_types to just specify the required arguments and their types.

PARAMETER	DESCRIPTION
args_schema	The schema for the tool. TYPE: type[BaseModel] | None DEFAULT: None
name	The name of the tool. TYPE: str | None DEFAULT: None
description	The description of the tool. TYPE: str | None DEFAULT: None
arg_types	A dictionary of argument names to types. TYPE: dict[str, type] | None DEFAULT: None

RETURNS	DESCRIPTION
BaseTool	A BaseTool instance.

Typed dict input:

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda


class Args(TypedDict):
    a: int
    b: list[int]


def f(x: Args) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via args_schema:

from typing import Any
from pydantic import BaseModel, Field
from langchain_core.runnables import RunnableLambda

def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

class FSchema(BaseModel):
    """Apply a function to an integer and list of integers."""

    a: int = Field(..., description="Integer")
    b: list[int] = Field(..., description="List of ints")

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via arg_types:

from typing import Any
from langchain_core.runnables import RunnableLambda


def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})

str input:

from langchain_core.runnables import RunnableLambda


def f(x: str) -> str:
    return x + "a"


def g(x: str) -> str:
    return x + "z"


runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")

__init__

__init__(*args: Any, **kwargs: Any) -> None

is_lc_serializable classmethod

is_lc_serializable() -> bool

Is this class serializable?

By design, even if a class inherits from Serializable, it is not serializable by default. This is to prevent accidental serialization of objects that should not be serialized.

RETURNS	DESCRIPTION
bool	Whether the class is serializable. Default is False.

get_lc_namespace classmethod

get_lc_namespace() -> list[str]

Get the namespace of the LangChain object.

For example, if the class is langchain.llms.openai.OpenAI, then the namespace is ["langchain", "llms", "openai"]

RETURNS	DESCRIPTION
list[str]	The namespace.

lc_id classmethod

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object.

For example, for the class langchain.llms.openai.OpenAI, the id is ["langchain", "llms", "openai", "OpenAI"].

to_json

to_json() -> SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

RETURNS	DESCRIPTION
SerializedConstructor | SerializedNotImplemented	A JSON-serializable representation of the Runnable.

to_json_not_implemented

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

RETURNS	DESCRIPTION
SerializedNotImplemented	SerializedNotImplemented.

configurable_fields

configurable_fields(
    **kwargs: AnyConfigurableField,
) -> RunnableSerializable[Input, Output]

Configure particular Runnable fields at runtime.

PARAMETER	DESCRIPTION
**kwargs	A dictionary of ConfigurableField instances to configure. TYPE: AnyConfigurableField DEFAULT: {}

RAISES	DESCRIPTION
ValueError	If a configuration key is not found in the Runnable.

RETURNS	DESCRIPTION
RunnableSerializable[Input, Output]	A new Runnable with the fields configured.

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print("max_tokens_20: ", model.invoke("tell me something about chess").content)

# max_tokens = 200
print(
    "max_tokens_200: ",
    model.with_config(configurable={"output_token_number": 200})
    .invoke("tell me something about chess")
    .content,
)

configurable_alternatives

configurable_alternatives(
    which: ConfigurableField,
    *,
    default_key: str = "default",
    prefix_keys: bool = False,
    **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]],
) -> RunnableSerializable[Input, Output]

Configure alternatives for Runnable objects that can be set at runtime.

PARAMETER	DESCRIPTION
which	The ConfigurableField instance that will be used to select the alternative. TYPE: ConfigurableField
default_key	The default key to use if no alternative is selected. TYPE: str DEFAULT: 'default'
prefix_keys	Whether to prefix the keys with the ConfigurableField id. TYPE: bool DEFAULT: False
**kwargs	A dictionary of keys to Runnable instances or callables that return Runnable instances. TYPE: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]] DEFAULT: {}

RETURNS	DESCRIPTION
RunnableSerializable[Input, Output]	A new Runnable with the alternatives configured.

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-sonnet-4-5-20250929"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI(),
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

# uses ChatOpenAI
print(
    model.with_config(configurable={"llm": "openai"})
    .invoke("which organization created you?")
    .content
)

CohereRerank

Bases: BaseDocumentCompressor

Document compressor that uses Cohere Rerank API.

METHOD	DESCRIPTION
acompress_documents	Async compress retrieved documents given the query context.
validate_environment	Validate that api key and python package exists in environment.
validate_model_specified	Validate that model is specified.
rerank	Returns an ordered list of documents ordered by their relevance to the
compress_documents	Compress documents using Cohere's rerank API.

client class-attribute instance-attribute

client: Any = None

Cohere client to use for compressing documents.

top_n class-attribute instance-attribute

top_n: int | None = 3

Number of documents to return.

model class-attribute instance-attribute

model: str | None = None

Model to use for reranking. Mandatory to specify the model name.

cohere_api_key class-attribute instance-attribute

cohere_api_key: SecretStr | None = Field(
    default_factory=secret_from_env("COHERE_API_KEY", default=None)
)

Cohere API key. Must be specified directly or via environment variable COHERE_API_KEY.

base_url class-attribute instance-attribute

base_url: str | None = None

Override the default Cohere API URL.

user_agent class-attribute instance-attribute

user_agent: str = 'langchain:partner'

Identifier for the application making the request.

acompress_documents async

acompress_documents(
    documents: Sequence[Document], query: str, callbacks: Callbacks | None = None
) -> Sequence[Document]

Async compress retrieved documents given the query context.

PARAMETER	DESCRIPTION
documents	The retrieved Document objects. TYPE: Sequence[Document]
query	The query context. TYPE: str
callbacks	Optional Callbacks to run during compression. TYPE: Callbacks | None DEFAULT: None

RETURNS	DESCRIPTION
Sequence[Document]	The compressed documents.

validate_environment

validate_environment() -> Self

Validate that api key and python package exists in environment.

validate_model_specified

validate_model_specified() -> Self

Validate that model is specified.

rerank

rerank(
    documents: Sequence[str | Document | dict],
    query: str,
    *,
    rank_fields: Sequence[str] | None = None,
    model: str | None = None,
    top_n: int | None = -1,
    max_tokens_per_doc: int | None = None,
) -> list[dict[str, Any]]

Returns an ordered list of documents ordered by their relevance to the provided query.

PARAMETER	DESCRIPTION
query	The query to use for reranking. TYPE: str
documents	A sequence of documents to rerank. TYPE: Sequence[str | Document | dict]
rank_fields	A sequence of keys to use for reranking. TYPE: Sequence[str] | None DEFAULT: None
top_n	The number of results to return. If None, returns all results. Defaults to self.top_n.
max_tokens_per_doc	Documents will be truncated to the specified number of tokens. Defaults to 4000.

compress_documents

compress_documents(
    documents: Sequence[Document], query: str, callbacks: Callbacks | None = None
) -> Sequence[Document]

Compress documents using Cohere's rerank API.

PARAMETER	DESCRIPTION
documents	A sequence of documents to compress. TYPE: Sequence[Document]
query	The query to use for compressing the documents. TYPE: str
callbacks	Callbacks to run during the compression process. TYPE: Callbacks | None DEFAULT: None

RETURNS	DESCRIPTION
Sequence[Document]	A sequence of compressed documents.

create_cohere_react_agent

create_cohere_react_agent(
    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate
) -> Runnable

Create an agent that enables multiple tools to be used in sequence to complete a task.

PARAMETER	DESCRIPTION
llm	The ChatCohere LLM instance to use. TYPE: BaseLanguageModel
tools	Tools this agent has access to. TYPE: Sequence[BaseTool]
prompt	The prompt to use. TYPE: ChatPromptTemplate

RETURNS	DESCRIPTION
Runnable	A Runnable sequence representing an agent. It takes as input all the same input
Runnable	variables as the prompt passed in does and returns a list[AgentAction] or a
Runnable	single AgentFinish.
Runnable	The AgentFinish will have two fields:
Runnable	output: str - The output string generated by the model
Runnable	citations: list[CohereCitation] - A list of citations that refer to the output and observations made by the agent. If there are no citations this list will be empty.

Example

from langchain.agents import AgentExecutor
from langchain.prompts import ChatPromptTemplate
from langchain_cohere import ChatCohere, create_cohere_react_agent

prompt = ChatPromptTemplate.from_template("{input}")
tools = []  # Populate this with a list of tools you would like to use.

llm = ChatCohere()

agent = create_cohere_react_agent(
    llm,
    tools,
    prompt
)
agent_executor = AgentExecutor(agent=agent, tools=tools)

agent_executor.invoke({
    "input": "In what year was the company that was founded as Sound of Music added to the S&P 500?",
})