`AnthropicLLM` (Legacy LLM)¶

Reference docs

This page contains reference documentation for the legacy AnthropicLLM LLM. See the docs for conceptual guides, tutorials, and examples on using AnthropicLLM.

langchain_anthropic.llms.AnthropicLLM ¶

Bases: LLM, _AnthropicCommon

Anthropic large language model.

To use, you should have the environment variable ANTHROPIC_API_KEY set with your API key, or pass it as a named parameter to the constructor.

Example

from langchain_anthropic import AnthropicLLM

model = AnthropicLLM()

METHOD	DESCRIPTION
`raise_warning`	Raise warning that this class is deprecated.
`is_lc_serializable`	Whether this class can be serialized by langchain.
`convert_prompt`	Convert a `PromptValue` to a string.
`get_num_tokens`	Calculate number of tokens.
`get_name`	Get the name of the `Runnable`.
`get_input_schema`	Get a Pydantic model that can be used to validate input to the `Runnable`.
`get_input_jsonschema`	Get a JSON schema that represents the input to the `Runnable`.
`get_output_schema`	Get a Pydantic model that can be used to validate output to the `Runnable`.
`get_output_jsonschema`	Get a JSON schema that represents the output of the `Runnable`.
`config_schema`	The type of config this `Runnable` accepts specified as a Pydantic model.
`get_config_jsonschema`	Get a JSON schema that represents the config of the `Runnable`.
`get_graph`	Return a graph representation of this `Runnable`.
`get_prompts`	Return a list of prompts used by this `Runnable`.
`__or__`	Runnable "or" operator.
`__ror__`	Runnable "reverse-or" operator.
`pipe`	Pipe `Runnable` objects.
`pick`	Pick keys from the output `dict` of this `Runnable`.
`assign`	Assigns new fields to the `dict` output of this `Runnable`.
`invoke`	Transform a single input into an output.
`ainvoke`	Transform a single input into an output.
`batch`	Default implementation runs invoke in parallel using a thread pool executor.
`batch_as_completed`	Run `invoke` in parallel on a list of inputs.
`abatch`	Default implementation runs `ainvoke` in parallel using `asyncio.gather`.
`abatch_as_completed`	Run `ainvoke` in parallel on a list of inputs.
`stream`	Default implementation of `stream`, which calls `invoke`.
`astream`	Default implementation of `astream`, which calls `ainvoke`.
`astream_log`	Stream all output from a `Runnable`, as reported to the callback system.
`astream_events`	Generate a stream of events.
`transform`	Transform inputs to outputs.
`atransform`	Transform inputs to outputs.
`bind`	Bind arguments to a `Runnable`, returning a new `Runnable`.
`with_config`	Bind config to a `Runnable`, returning a new `Runnable`.
`with_listeners`	Bind lifecycle listeners to a `Runnable`, returning a new `Runnable`.
`with_alisteners`	Bind async lifecycle listeners to a `Runnable`.
`with_types`	Bind input and output types to a `Runnable`, returning a new `Runnable`.
`with_retry`	Create a new `Runnable` that retries the original `Runnable` on exceptions.
`map`	Return a new `Runnable` that maps a list of inputs to a list of outputs.
`with_fallbacks`	Add fallbacks to a `Runnable`, returning a new `Runnable`.
`as_tool`	Create a `BaseTool` from a `Runnable`.
`__init__`
`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_from_messages`	Get the number of tokens in the messages.
`validate_environment`	Validate that api key and python package exists in environment.
`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.

lc_secrets `property` ¶

lc_secrets: dict[str, str]

Return a mapping of secret keys to environment variables.

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_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.

model `class-attribute` `instance-attribute` ¶

model: str = Field(default='claude-3-5-sonnet-latest', alias='model_name')

Model name to use.

max_tokens `class-attribute` `instance-attribute` ¶

max_tokens: int = Field(default=1024, alias='max_tokens_to_sample')

Denotes the number of tokens to predict per generation.

temperature `class-attribute` `instance-attribute` ¶

temperature: float | None = None

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

top_k `class-attribute` `instance-attribute` ¶

top_k: int | None = None

Number of most likely tokens to consider at each step.

top_p `class-attribute` `instance-attribute` ¶

top_p: float | None = None

Total probability mass of tokens to consider at each step.

streaming `class-attribute` `instance-attribute` ¶

streaming: bool = False

Whether to stream the results.

default_request_timeout `class-attribute` `instance-attribute` ¶

default_request_timeout: float | None = None

Timeout for requests to Anthropic Completion API. Default is 600 seconds.

max_retries `class-attribute` `instance-attribute` ¶

max_retries: int = 2

Number of retries allowed for requests sent to the Anthropic Completion API.

anthropic_api_url `class-attribute` `instance-attribute` ¶

anthropic_api_url: str | None = Field(
    alias="base_url",
    default_factory=from_env("ANTHROPIC_API_URL", default="https://api.anthropic.com"),
)

Base URL for API requests. Only specify if using a proxy or service emulator.

If a value isn't passed in, will attempt to read the value from ANTHROPIC_API_URL. If not set, the default value https://api.anthropic.com will be used.

anthropic_api_key `class-attribute` `instance-attribute` ¶

anthropic_api_key: SecretStr = Field(
    alias="api_key", default_factory=secret_from_env("ANTHROPIC_API_KEY", default="")
)

Automatically read from env var ANTHROPIC_API_KEY if not provided.

raise_warning `classmethod` ¶

raise_warning(values: dict) -> Any

Raise warning that this class is deprecated.

is_lc_serializable `classmethod` ¶

is_lc_serializable() -> bool

Whether this class can be serialized by langchain.

convert_prompt ¶

convert_prompt(prompt: PromptValue) -> str

Convert a PromptValue to a string.

get_num_tokens ¶

get_num_tokens(text: str) -> int

Calculate number of tokens.

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

prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
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 the `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 the `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 the `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 the `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 the `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 the `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"]})

For instance:

from langchain_core.runnables import RunnableLambda


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


chain = RunnableLambda(func=reverse)

events = [event async for event in chain.astream_events("hello", version="v2")]

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

Example: Dispatch Custom Event

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


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

slow_thing = RunnableLambda(slow_thing)

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

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

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

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

transform ¶

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

Transform inputs to outputs.

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

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

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

YIELDS	DESCRIPTION
`Output`	The output of the `Runnable`.

atransform `async` ¶

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

Transform inputs to outputs.

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

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

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

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

bind ¶

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

Bind arguments to a Runnable, returning a new Runnable.

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

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

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

Example

from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser

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

# Without bind
chain = model | StrOutputParser()

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

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

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

with_config ¶

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

Bind config to a Runnable, returning a new Runnable.

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

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

with_listeners ¶

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

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

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

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

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

Example

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

import time


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


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


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


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

with_alisteners ¶

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

Bind async lifecycle listeners to a Runnable.

Returns a new Runnable.

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

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

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

Example

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

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

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

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

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

runnable = RunnableLambda(test_runnable).with_alisteners(
    on_start=fn_start,
    on_end=fn_end
)
async def concurrent_runs():
    await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))

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

with_types ¶

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

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

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

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

with_retry ¶

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

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

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

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

Example

from langchain_core.runnables import RunnableLambda

count = 0


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


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

assert count == 2

map ¶

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

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

Calls invoke with each input.

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

Example

from langchain_core.runnables import RunnableLambda


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


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

with_fallbacks ¶

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

Add fallbacks to a Runnable, returning a new Runnable.

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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	If `string` is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If `None`, exceptions will not be passed to fallbacks. If used, the base `Runnable` and its fallbacks must accept a dictionary as input. TYPE: `str \| None` DEFAULT: `None`

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

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


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


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


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

PARAMETER	DESCRIPTION
`fallbacks`	A sequence of runnables to try if the original `Runnable` fails. TYPE: `Sequence[Runnable[Input, Output]]`
`exceptions_to_handle`	A tuple of exception types to handle. TYPE: `tuple[type[BaseException], ...]` DEFAULT: `(Exception,)`
`exception_key`	If `string` is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If `None`, exceptions will not be passed to fallbacks. If used, the base `Runnable` and its fallbacks must accept a dictionary as input. TYPE: `str \| None` DEFAULT: `None`

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

as_tool ¶

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

Create a BaseTool from a Runnable.

as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Where possible, schemas are inferred from runnable.get_input_schema. Alternatively (e.g., if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. You can also pass arg_types to just specify the required arguments and their types.

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

RETURNS	DESCRIPTION
`BaseTool`	A `BaseTool` instance.

Typed dict input:

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda


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


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


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

dict input, specifying schema via args_schema:

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

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

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

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

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

dict input, specifying schema via arg_types:

from typing import Any
from langchain_core.runnables import RunnableLambda


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


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

String input:

from langchain_core.runnables import RunnableLambda


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


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


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

init ¶

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

get_lc_namespace `classmethod` ¶

get_lc_namespace() -> list[str]

Get the namespace of the LangChain object.

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

RETURNS	DESCRIPTION
`list[str]`	The namespace.

lc_id `classmethod` ¶

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

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

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

to_json ¶

to_json() -> SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

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

to_json_not_implemented ¶

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

RETURNS	DESCRIPTION
`SerializedNotImplemented`	`SerializedNotImplemented`.

configurable_fields ¶

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

Configure particular Runnable fields at runtime.

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

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

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

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

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

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

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

configurable_alternatives ¶

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

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

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

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

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

model = ChatAnthropic(
    model_name="claude-3-7-sonnet-20250219"
).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:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
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:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
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_from_messages ¶

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

Get the number of tokens in the messages.

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

Note

The base implementation of get_num_tokens_from_messages ignores tool schemas.

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

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

validate_environment ¶

validate_environment() -> Self

Validate that api key and python package exists in environment.

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:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
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:

Take advantage of batched calls,
Need more output from the model than just the top generated value,
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")

AnthropicLLM (Legacy LLM)¶

langchain_anthropic.llms.AnthropicLLM ¶

lc_secrets property ¶

name class-attribute instance-attribute ¶

InputType property ¶

OutputType property ¶

input_schema property ¶

output_schema property ¶

config_specs property ¶

lc_attributes property ¶

cache class-attribute instance-attribute ¶

verbose class-attribute instance-attribute ¶

callbacks class-attribute instance-attribute ¶

tags class-attribute instance-attribute ¶

metadata class-attribute instance-attribute ¶

custom_get_token_ids class-attribute instance-attribute ¶

model class-attribute instance-attribute ¶

max_tokens class-attribute instance-attribute ¶

temperature class-attribute instance-attribute ¶

top_k class-attribute instance-attribute ¶

top_p class-attribute instance-attribute ¶

streaming class-attribute instance-attribute ¶

default_request_timeout class-attribute instance-attribute ¶

max_retries class-attribute instance-attribute ¶

anthropic_api_url class-attribute instance-attribute ¶

anthropic_api_key class-attribute instance-attribute ¶

raise_warning classmethod ¶

is_lc_serializable classmethod ¶

convert_prompt ¶

get_num_tokens ¶

get_name ¶

get_input_schema ¶

get_input_jsonschema ¶

get_output_schema ¶

get_output_jsonschema ¶

config_schema ¶

get_config_jsonschema ¶

get_graph ¶

get_prompts ¶

__or__ ¶

__ror__ ¶

pipe ¶

pick ¶

assign ¶

invoke ¶

ainvoke async ¶

batch ¶

batch_as_completed ¶

abatch async ¶

abatch_as_completed async ¶

stream ¶

astream async ¶

astream_log async ¶

astream_events async ¶

transform ¶

atransform async ¶

bind ¶

with_config ¶

with_listeners ¶

with_alisteners ¶

with_types ¶

with_retry ¶

map ¶

with_fallbacks ¶

as_tool ¶

__init__ ¶

get_lc_namespace classmethod ¶

lc_id classmethod ¶

to_json ¶

to_json_not_implemented ¶

configurable_fields ¶

configurable_alternatives ¶

set_verbose ¶

generate_prompt ¶

agenerate_prompt async ¶

with_structured_output ¶

get_token_ids ¶

get_num_tokens_from_messages ¶

validate_environment ¶

generate ¶

`AnthropicLLM` (Legacy LLM)¶

lc_secrets `property` ¶

name `class-attribute` `instance-attribute` ¶

InputType `property` ¶

OutputType `property` ¶

input_schema `property` ¶

output_schema `property` ¶

config_specs `property` ¶

lc_attributes `property` ¶

cache `class-attribute` `instance-attribute` ¶

verbose `class-attribute` `instance-attribute` ¶

callbacks `class-attribute` `instance-attribute` ¶

tags `class-attribute` `instance-attribute` ¶

metadata `class-attribute` `instance-attribute` ¶

custom_get_token_ids `class-attribute` `instance-attribute` ¶

model `class-attribute` `instance-attribute` ¶

max_tokens `class-attribute` `instance-attribute` ¶

temperature `class-attribute` `instance-attribute` ¶

top_k `class-attribute` `instance-attribute` ¶

top_p `class-attribute` `instance-attribute` ¶

streaming `class-attribute` `instance-attribute` ¶

default_request_timeout `class-attribute` `instance-attribute` ¶

max_retries `class-attribute` `instance-attribute` ¶

anthropic_api_url `class-attribute` `instance-attribute` ¶

anthropic_api_key `class-attribute` `instance-attribute` ¶

raise_warning `classmethod` ¶

is_lc_serializable `classmethod` ¶

or ¶

ror ¶

ainvoke `async` ¶

abatch `async` ¶

abatch_as_completed `async` ¶

astream `async` ¶

astream_log `async` ¶

astream_events `async` ¶

atransform `async` ¶

init ¶

get_lc_namespace `classmethod` ¶

lc_id `classmethod` ¶

agenerate_prompt `async` ¶

agenerate `async` ¶

str ¶