langchain-nvidia-ai-endpoints

langchain_nvidia_ai_endpoints

LangChain NVIDIA AI Foundation Model Playground Integration

Note

You can import langchain_nvidia instead.

This comprehensive module integrates NVIDIA's state-of-the-art AI Foundation Models, featuring advanced models for conversational AI and semantic embeddings, into the LangChain framework. It provides robust classes for seamless interaction with NVIDIA's AI models, particularly tailored for enriching conversational experiences and enhancing semantic understanding in various applications.

Features

1. Chat Models (ChatNVIDIA): This class serves as the primary interface for interacting with NVIDIA's Foundation chat models. Users can effortlessly utilize NVIDIA's advanced models like 'Mistral' to engage in rich, context-aware conversations, applicable across diverse domains from customer support to interactive storytelling.

2. Semantic Embeddings (NVIDIAEmbeddings): The module offers capabilities to generate sophisticated embeddings using NVIDIA's AI models. These embeddings are instrumental for tasks like semantic analysis, text similarity assessments, and contextual understanding, significantly enhancing the depth of NLP applications.

Installation

Install this module easily using pip:

pip install langchain-nvidia-ai-endpoints

Utilizing Chat Models

After setting up the environment, interact with NVIDIA AI Foundation models::

from langchain_nvidia_ai_endpoints import ChatNVIDIA

ai_chat_model = ChatNVIDIA(model="meta/llama2-70b")
response = ai_chat_model.invoke("Tell me about the LangChain integration.")

Generating Semantic Embeddings

Use NVIDIA's models for creating embeddings, useful in various NLP tasks::

from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings

embed_model = NVIDIAEmbeddings(model="nvolveqa_40k")
embedding_output = embed_model.embed_query("Exploring AI capabilities.")

FUNCTION	DESCRIPTION
register_model	Register a model as a known model.

Model

Bases: BaseModel

Model information.

ATTRIBUTE	DESCRIPTION
id	Unique identifier for the model, passed as model parameter for requests TYPE: str
model_type	API type TYPE: Literal['chat', 'vlm', 'nv-vlm', 'embedding', 'ranking', 'completions', 'qa'] | None
client	Client name TYPE: Literal['ChatNVIDIA', 'NVIDIAEmbeddings', 'NVIDIARerank', 'NVIDIA'] | None
endpoint	Custom endpoint for the model TYPE: str | None
aliases	List of aliases for the model TYPE: list | None
supports_tools	Whether the model supports tool calling TYPE: bool | None
supports_structured_output	Whether the model supports structured output TYPE: bool | None
supports_thinking	Whether the model supports thinking mode TYPE: bool | None

All aliases are deprecated and will trigger a warning when used.

ChatNVIDIA

Bases: BaseChatModel

NVIDIA chat model.

Example

from langchain_nvidia_ai_endpoints import ChatNVIDIA


model = ChatNVIDIA(model="meta/llama2-70b")
response = model.invoke("Hello")

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.
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.
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	Get the number of tokens present in the 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.
__init__	Create a new NVIDIAChat chat model.
get_available_models	Get a list of available models that work with ChatNVIDIA.
bind_tools	Bind tools to the model.
with_structured_output	Bind a structured output schema to the model.
with_thinking_mode	Configure the model to use thinking mode.

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.

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 class-attribute instance-attribute

profile: ModelProfile | None = Field(default=None, exclude=True)

Profile detailing model capabilities.

Beta feature

This is a beta feature. The format of model profiles is subject to change.

If not specified, automatically loaded from the provider package on initialization if data is available.

Example profile data includes context window sizes, supported modalities, or support for tool calling, structured output, and other features.

Added in langchain-core 1.1

available_models property

available_models: list[Model]

Get a list of available models that work with ChatNVIDIA.

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"]})

Example

from langchain_core.runnables import RunnableLambda


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


chain = RunnableLambda(func=reverse)

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

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.

TypedDict 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")

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.

Example

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.

Example

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

get_num_tokens(text: str) -> int

Get the number of tokens present in the text.

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

This should be overridden by model-specific implementations to provide accurate token counts via model-specific tokenizers.

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

RETURNS	DESCRIPTION
int	The integer number of tokens 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.

This should be overridden by model-specific implementations to provide accurate token counts via model-specific tokenizers.

Note

• The base implementation of get_num_tokens_from_messages ignores tool schemas.
• The base implementation of get_num_tokens_from_messages adds additional prefixes to messages in represent user roles, which will add to the overall token count. Model-specific implementations may choose to handle this differently.

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.

__init__

__init__(**kwargs: Any)

Create a new NVIDIAChat chat model.

This class provides access to a NVIDIA NIM for chat. By default, it connects to a hosted NIM, but can be configured to connect to a local NIM using the base_url parameter.

An API key is required to connect to the hosted NIM.

PARAMETER	DESCRIPTION
model	The model to use for chat. TYPE: str
nvidia_api_key	The API key to use for connecting to the hosted NIM. TYPE: str
api_key	Alternative to nvidia_api_key. TYPE: str
base_url	The base URL of the NIM to connect to. Format for base URL is http://host:port TYPE: str
temperature	Sampling temperature in [0, 1]. TYPE: float
max_tokens	Maximum number of tokens to generate. Deprecated, use max_completion_tokens instead. max_tokens and max_completion_tokens are aliases. If both max_tokens and max_completion_tokens are supplied, max_completion_tokens takes precedence. TYPE: int
max_completion_tokens	Maximum number of tokens to generate. TYPE: int
top_p	Top-p for distribution sampling. TYPE: float
seed	A seed for deterministic results. TYPE: int
stop	A list of cased stop words. TYPE: list[str]
min_tokens	Minimum number of tokens to generate. TYPE: int
ignore_eos	Whether to ignore end-of-sequence tokens. TYPE: bool
default_headers	Default headers merged into all requests. TYPE: dict[str, str]

The recommended way to provide the API key is through the NVIDIA_API_KEY environment variable.

Base URL:

• Connect to a self-hosted model with NVIDIA NIM using the base_url arg to link to the local host at localhost:8000:

  llm = ChatNVIDIA(
      base_url="http://localhost:8000/v1",
      model="meta-llama3-8b-instruct"
  )
  

get_available_models classmethod

get_available_models(**kwargs: Any) -> list[Model]

Get a list of available models that work with ChatNVIDIA.

bind_tools

bind_tools(
    tools: Sequence[dict[str, Any] | Type | Callable | BaseTool],
    *,
    tool_choice: dict
    | str
    | Literal["auto", "none", "any", "required"]
    | bool
    | None = None,
    **kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]

Bind tools to the model.

Note

The strict mode is always in effect, if you need it disabled, please file an issue.

PARAMETER	DESCRIPTION
tools	A list of tools to bind to the model. TYPE: list
tool_choice	Control tool choice. Options: 'any' or 'required' – Force a tool call. 'auto' – Let the model decide. 'none' – Force no tool call. str or dict – Force a specific tool call. bool – If True, force a tool call; if False, force no tool call. Defaults to passing no value. TYPE: dict | str | Literal['auto', 'none', 'any', 'required'] | bool | None DEFAULT: None
**kwargs	Additional keyword arguments. TYPE: Any DEFAULT: {}

with_structured_output

with_structured_output(
    schema: dict | Type, *, include_raw: bool = False, **kwargs: Any
) -> Runnable[LanguageModelInput, dict | BaseModel]

Bind a structured output schema to the model.

PARAMETER	DESCRIPTION
schema	The schema to bind to the model. TYPE: dict | Type
include_raw	Always False. Passing True raises an error. TYPE: bool DEFAULT: False
**kwargs	Additional keyword arguments. TYPE: Any DEFAULT: {}

Note

• The strict mode is always in effect, if you need it disabled, please file an issue.
• If you need include_raw=True consider using an unstructured model and output formatter, or file an issue.

The schema can be:

1. A dictionary representing a JSON schema
2. A Pydantic object
3. An Enum

If a dictionary is provided, the model will return a dictionary.

Dictionary schema

json_schema = {
    "title": "joke",
    "description": "Joke to tell user.",
    "type": "object",
    "properties": {
        "setup": {
            "type": "string",
            "description": "The setup of the joke",
        },
        "punchline": {
            "type": "string",
            "description": "The punchline to the joke",
        },
    },
    "required": ["setup", "punchline"],
}

structured_llm = llm.with_structured_output(json_schema)
structured_llm.invoke("Tell me a joke about NVIDIA")
# Output: {'setup': 'Why did NVIDIA go broke? The hardware ate all the software.',
#          'punchline': 'It took a big bite out of their main board.'}

If a Pydantic schema is provided, the model will return a Pydantic object.

Pydantic schema

from pydantic import BaseModel, Field
class Joke(BaseModel):
    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke")

structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about NVIDIA")
# Output: Joke(setup='Why did NVIDIA go broke? The hardware ate all the software.',
#              punchline='It took a big bite out of their main board.')

If an Enum is provided, all values must be strings, and the model will return an Enum object.

Enum schema

import enum
class Choices(enum.Enum):
    A = "A"
    B = "B"
    C = "C"

structured_llm = llm.with_structured_output(Choices)
structured_llm.invoke("What is the first letter in this list? [X, Y, Z, C]")
# Output: <Choices.C: 'C'>

Streaming

Unlike other streaming responses, the streamed chunks will be increasingly complete. They will not be deltas. The last chunk will contain the complete response.

For instance with a dictionary schema, the chunks will be:

structured_llm = llm.with_structured_output(json_schema)
for chunk in structured_llm.stream("Tell me a joke about NVIDIA"):
    print(chunk)

# Output:
# {}
# {'setup': ''}
# {'setup': 'Why'}
# {'setup': 'Why did'}
# {'setup': 'Why did N'}
# {'setup': 'Why did NVID'}
# ...
# {'setup': 'Why did NVIDIA go broke? The hardware ate all the software.', 'punchline': 'It took a big bite out of their main board'}
# {'setup': 'Why did NVIDIA go broke? The hardware ate all the software.', 'punchline': 'It took a big bite out of their main board.'}

For instance with a Pydantic schema, the chunks will be:

structured_llm = llm.with_structured_output(Joke)
for chunk in structured_llm.stream("Tell me a joke about NVIDIA"):
    print(chunk)

# Output:
# setup='Why did NVIDIA go broke? The hardware ate all the software.' punchline=''
# setup='Why did NVIDIA go broke? The hardware ate all the software.' punchline='It'
# setup='Why did NVIDIA go broke? The hardware ate all the software.' punchline='It took'
# ...
# setup='Why did NVIDIA go broke? The hardware ate all the software.' punchline='It took a big bite out of their main board'
# setup='Why did NVIDIA go broke? The hardware ate all the software.' punchline='It took a big bite out of their main board.'

For Pydantic schema and Enum, the output will be None if the response is insufficient to construct the object or otherwise invalid.

llm = ChatNVIDIA(max_completion_tokens=1)
structured_llm = llm.with_structured_output(Joke)
print(structured_llm.invoke("Tell me a joke about NVIDIA"))

# Output: None

For more, see docs on structured output.

with_thinking_mode

with_thinking_mode(
    enabled: bool = True, **kwargs: Any
) -> Runnable[LanguageModelInput, BaseMessage]

Configure the model to use thinking mode.

PARAMETER	DESCRIPTION
enabled	Whether to enable thinking mode. TYPE: bool DEFAULT: True
**kwargs	Additional keyword arguments. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
Runnable[LanguageModelInput, BaseMessage]	A runnable that will use thinking mode when enabled.

Example

from langchain_nvidia_ai_endpoints import ChatNVIDIA

model = ChatNVIDIA(model="nvidia/llama-3.1-nemotron-nano-8b-v1")

# Enable thinking mode
thinking_model = model.with_thinking_mode(enabled=True)
response = thinking_model.invoke("Hello")

# Disable thinking mode
no_thinking_model = model.with_thinking_mode(enabled=False)
response = no_thinking_model.invoke("Hello")

NVIDIAEmbeddings

Bases: BaseModel, Embeddings

Client to NVIDIA embeddings models.

ATTRIBUTE	DESCRIPTION
model	The name of the model to use TYPE: str | None
truncate	'NONE', 'START', 'END', truncate input text if it exceeds the model's maximum token length. Default is 'NONE', which raises an error if an input is too long. TYPE: Literal['NONE', 'START', 'END']
dimensions	The number of dimensions for the embeddings. This parameter is not supported by all models. TYPE: int | None

METHOD	DESCRIPTION
aembed_documents	Asynchronous Embed search docs.
aembed_query	Asynchronous Embed query text.
__init__	Create a new NVIDIAEmbeddings embedder.
get_available_models	Get a list of available models that work with NVIDIAEmbeddings.
embed_query	Input pathway for query embeddings.
embed_documents	Input pathway for document embeddings.

available_models property

available_models: list[Model]

Get a list of available models that work with NVIDIAEmbeddings.

aembed_documents async

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

Asynchronous Embed search docs.

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

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings.

aembed_query async

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

Asynchronous Embed query text.

PARAMETER	DESCRIPTION
text	Text to embed. TYPE: str

RETURNS	DESCRIPTION
list[float]	Embedding.

__init__

__init__(**kwargs: Any)

Create a new NVIDIAEmbeddings embedder.

This class provides access to a NVIDIA NIM for embedding. By default, it connects to a hosted NIM, but can be configured to connect to a local NIM using the base_url parameter.

An API key is required to connect to the hosted NIM.

PARAMETER	DESCRIPTION
model	The model to use for embedding. TYPE: str
nvidia_api_key	The API key to use for connecting to the hosted NIM. TYPE: str
api_key	Alternative to nvidia_api_key. TYPE: str
base_url	The base URL of the NIM to connect to. Format for base URL is http://host:port TYPE: str
trucate	'NONE', 'START', 'END', truncate input text if it exceeds the model's context length. Default is 'NONE', which raises an error if an input is too long. TYPE: str
dimensions	The number of dimensions for the embeddings. This parameter is not supported by all models. TYPE: int
default_headers	Default headers merged into all requests. TYPE: dict[str, str]

The recommended way to provide the API key is through the NVIDIA_API_KEY environment variable.

Base URL:

• Connect to a self-hosted model with NVIDIA NIM using the base_url arg to link to the local host at localhost:8000:

  embedder = NVIDIAEmbeddings(base_url="http://localhost:8080/v1")
  

get_available_models classmethod

get_available_models(**kwargs: Any) -> list[Model]

Get a list of available models that work with NVIDIAEmbeddings.

embed_query

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

Input pathway for query embeddings.

embed_documents

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

Input pathway for document embeddings.

NVIDIA

Bases: LLM

LangChain LLM that uses the Completions API with NVIDIA NIMs.

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.
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.
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.
with_structured_output	Not implemented on this class.
get_token_ids	Return the ordered IDs of the tokens in a text.
get_num_tokens	Get the number of tokens present in the text.
get_num_tokens_from_messages	Get the number of tokens in the messages.
generate	Pass a sequence of prompts to a model and return generations.
agenerate	Asynchronously pass a sequence of prompts to a model and return generations.
__str__	Return a string representation of the object for printing.
dict	Return a dictionary of the LLM.
save	Save the LLM.
__init__	Create a new NVIDIA LLM for Completions APIs.
get_available_models	Get a list of available models that work with the Completions API.

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: type[str]

Get the input 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.

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.

available_models property

available_models: list[Model]

Get a list of available models that work with NVIDIA.

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,
) -> str

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,
) -> str

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[LanguageModelInput],
    config: RunnableConfig | list[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any,
) -> list[str]

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[LanguageModelInput],
    config: RunnableConfig | list[RunnableConfig] | None = None,
    *,
    return_exceptions: bool = False,
    **kwargs: Any,
) -> list[str]

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[str]

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[str]

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"]})

Example

from langchain_core.runnables import RunnableLambda


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


chain = RunnableLambda(func=reverse)

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

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.

TypedDict 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")

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.

Example

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.

Example

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 | list[Callbacks] | 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
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 | list[Callbacks] | None = 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.

with_structured_output

with_structured_output(
    schema: dict | type, **kwargs: Any
) -> Runnable[LanguageModelInput, dict | BaseModel]

Not implemented on this class.

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

get_num_tokens(text: str) -> int

Get the number of tokens present in the text.

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

This should be overridden by model-specific implementations to provide accurate token counts via model-specific tokenizers.

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

RETURNS	DESCRIPTION
int	The integer number of tokens 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.

This should be overridden by model-specific implementations to provide accurate token counts via model-specific tokenizers.

Note

• The base implementation of get_num_tokens_from_messages ignores tool schemas.
• The base implementation of get_num_tokens_from_messages adds additional prefixes to messages in represent user roles, which will add to the overall token count. Model-specific implementations may choose to handle this differently.

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

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
prompts	List of string prompts. TYPE: list[str]
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 | list[Callbacks] | None DEFAULT: None
tags	List of tags to associate with each prompt. If provided, the length of the list must match the length of the prompts list. TYPE: list[str] | list[list[str]] | None DEFAULT: None
metadata	List of metadata dictionaries to associate with each prompt. If provided, the length of the list must match the length of the prompts list. TYPE: dict[str, Any] | list[dict[str, Any]] | None DEFAULT: None
run_name	List of run names to associate with each prompt. If provided, the length of the list must match the length of the prompts list. TYPE: str | list[str] | None DEFAULT: None
run_id	List of run IDs to associate with each prompt. If provided, the length of the list must match the length of the prompts list. TYPE: UUID | list[UUID | None] | None DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RAISES	DESCRIPTION
ValueError	If prompts is not a list.
ValueError	If the length of callbacks, tags, metadata, or run_name (if provided) does not match the length of prompts.

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(
    prompts: list[str],
    stop: list[str] | None = None,
    callbacks: Callbacks | list[Callbacks] | None = None,
    *,
    tags: list[str] | list[list[str]] | None = None,
    metadata: dict[str, Any] | list[dict[str, Any]] | None = None,
    run_name: str | list[str] | None = None,
    run_id: UUID | list[UUID | None] | 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
prompts	List of string prompts. TYPE: list[str]
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 | list[Callbacks] | None DEFAULT: None
tags	List of tags to associate with each prompt. If provided, the length of the list must match the length of the prompts list. TYPE: list[str] | list[list[str]] | None DEFAULT: None
metadata	List of metadata dictionaries to associate with each prompt. If provided, the length of the list must match the length of the prompts list. TYPE: dict[str, Any] | list[dict[str, Any]] | None DEFAULT: None
run_name	List of run names to associate with each prompt. If provided, the length of the list must match the length of the prompts list. TYPE: str | list[str] | None DEFAULT: None
run_id	List of run IDs to associate with each prompt. If provided, the length of the list must match the length of the prompts list. TYPE: UUID | list[UUID | None] | None DEFAULT: None
**kwargs	Arbitrary additional keyword arguments. These are usually passed to the model provider API call. TYPE: Any DEFAULT: {}

RAISES	DESCRIPTION
ValueError	If the length of callbacks, tags, metadata, or run_name (if provided) does not match the length of prompts.

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

__str__

__str__() -> str

Return a string representation of the object for printing.

dict

dict(**kwargs: Any) -> dict

Return a dictionary of the LLM.

save

save(file_path: Path | str) -> None

Save the LLM.

PARAMETER	DESCRIPTION
file_path	Path to file to save the LLM to. TYPE: Path | str

RAISES	DESCRIPTION
ValueError	If the file path is not a string or Path object.

Example

llm.save(file_path="path/llm.yaml")

__check_kwargs

__check_kwargs(kwargs: dict[str, Any]) -> dict[str, Any]

Check kwargs, warn for unknown keys, and return a copy recognized keys.

__init__

__init__(**kwargs: Any)

Create a new NVIDIA LLM for Completions APIs.

This class provides access to a NVIDIA NIM for completions. By default, it connects to a hosted NIM, but can be configured to connect to a local NIM using the base_url parameter.

An API key is required to connect to the hosted NIM.

PARAMETER	DESCRIPTION
model	The model to use for completions. TYPE: str
nvidia_api_key	The API key to use for connecting to the hosted NIM. TYPE: str
api_key	Alternative to nvidia_api_key. TYPE: str
base_url	The base URL of the NIM to connect to. TYPE: str

The recommended way to provide the API key is through the NVIDIA_API_KEY environment variable.

Additional arguments that can be passed to the Completions API:

• max_tokens (int): The maximum number of tokens to generate.
• stop (str or List[str]): The stop sequence to use for generating completions.
• temperature (float): The temperature to use for generating completions.
• top_p (float): The top-p value to use for generating completions.
• frequency_penalty (float): The frequency penalty to apply to the completion.
• presence_penalty (float): The presence penalty to apply to the completion.
• seed (int): The seed to use for generating completions.

These additional arguments can also be passed with bind(), e.g. NVIDIA().bind(max_tokens=512), or pass directly to invoke() or stream(), e.g. NVIDIA().invoke("prompt", max_tokens=512).

get_available_models classmethod

get_available_models(**kwargs: Any) -> list[Model]

Get a list of available models that work with the Completions API.

NVIDIARerank

Bases: BaseDocumentCompressor

LangChain Document Compressor that uses the NVIDIA NeMo Retriever Reranking API.

METHOD	DESCRIPTION
acompress_documents	Async compress retrieved documents given the query context.
__init__	Create a new NVIDIARerank document compressor.
get_available_models	Get a list of available models that work with NVIDIARerank.
compress_documents	Compress documents using the NVIDIA NeMo Retriever Reranking microservice API.

available_models property

available_models: list[Model]

Get a list of available models that work with NVIDIARerank.

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.

__init__

__init__(**kwargs: Any)

Create a new NVIDIARerank document compressor.

This class provides access to a NVIDIA NIM for reranking. By default, it connects to a hosted NIM, but can be configured to connect to a local NIM using the base_url parameter.

An API key is required to connect to the hosted NIM.

PARAMETER	DESCRIPTION
model	The model to use for reranking. TYPE: str
nvidia_api_key	The API key to use for connecting to the hosted NIM. TYPE: str
api_key	Alternative to nvidia_api_key. TYPE: str
base_url	The base URL of the NIM to connect to. TYPE: str
truncate	'NONE', 'END', truncate input text if it exceeds the model's context length. Default is model dependent and is likely to raise an error if an input is too long. TYPE: str
default_headers	Default headers merged into all requests. TYPE: dict[str, str]
extra_headers	Deprecated. Use default_headers instead. TYPE: dict[str, str]

The recommended way to provide the API key is through the NVIDIA_API_KEY environment variable.

Base URL:

• Connect to a self-hosted model with NVIDIA NIM using the base_url arg to link to the local host at localhost:8000:

  ranker = NVIDIARerank(base_url="http://localhost:8000/v1")
  

Example

from langchain_nvidia_ai_endpoints import NVIDIARerank
from langchain_core.documents import Document

query = "What is the GPU memory bandwidth of H100 SXM?"
passages = [
    "The Hopper GPU is paired with the Grace CPU using NVIDIA's ultra-fast
    chip-to-chip interconnect, delivering 900GB/s of bandwidth, 7X faster
    than PCIe Gen5. This innovative design will deliver up to 30X higher
    aggregate system memory bandwidth to the GPU compared to today's fastest
    servers and up to 10X higher performance for applications running
    terabytes of data.",

    "A100 provides up to 20X higher performance over the prior generation
    and can be partitioned into seven GPU instances to dynamically adjust to
    shifting demands. The A100 80GB debuts the world's fastest memory
    bandwidth at over 2 terabytes per second (TB/s) to run the largest
    models and datasets.",

    "Accelerated servers with H100 deliver the compute power—along with 3
    terabytes per second (TB/s) of memory bandwidth per GPU and scalability
    with NVLink and NVSwitch™.",
]

client = NVIDIARerank(
    model="nvidia/nv-rerankqa-mistral-4b-v3",
    api_key="$API_KEY_REQUIRED_IF_EXECUTING_OUTSIDE_NGC"
)

response = client.compress_documents(
    query=query,
    documents=[Document(page_content=passage) for passage in passages]
)

print(f"Most relevant: {response[0].page_content}"
    f"Least relevant: {response[-1].page_content}"
)

# Most relevant: Accelerated servers with H100 deliver the compute
# power—along with 3 terabytes per second (TB/s) of memory bandwidth per GPU
# and scalability with NVLink and NVSwitch™.
# Least relevant: A100 provides up to 20X higher performance over the prior
# generation and can be partitioned into seven GPU instances to dynamically
# adjust to shifting demands. The A100 80GB debuts the world's fastest
# memory bandwidth at over 2 terabytes per second (TB/s) to run the largest
# models and datasets.

get_available_models classmethod

get_available_models(**kwargs: Any) -> list[Model]

Get a list of available models that work with NVIDIARerank.

compress_documents

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

Compress documents using the NVIDIA NeMo Retriever Reranking microservice 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.

register_model

register_model(model: Model) -> None

Register a model as a known model.

Must be done at the beginning of a program, at least before the model is used or available models are listed.

For instance:

from langchain_nvidia_ai_endpoints import register_model, Model


register_model(
    Model(
        id="my-custom-model-name",
        model_type="chat",
        client="ChatNVIDIA",
        endpoint="http://host:port/path-to-my-model"
    )
)
llm = ChatNVIDIA(model="my-custom-model-name")

Be sure that the id matches the model parameter the endpoint expects.

Supported model types are chat models, which must accept and produce chat completion payloads.

Supported model clients are ChatNVIDIA, for chat models.

Endpoint is required.

Use this instead of passing base_url to a client constructor when the model's endpoint supports inference and not /v1/models listing.

Use base_url when the model's endpoint supports /v1/models listing and inference on a known path, e.g. /v1/chat/completions.