`langchain-ollama`¶

Reference docs

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

langchain_ollama ¶

This is the langchain_ollama package.

Provides infrastructure for interacting with the Ollama service.

Note

Newly added in 0.3.4: validate_model_on_init param on all models. This parameter allows you to validate the model exists in Ollama locally on initialization. If set to True, it will raise an error if the model does not exist locally. This is useful for ensuring that the model is available before attempting to use it, especially in environments where models may not be pre-downloaded.

ChatOllama ¶

Bases: BaseChatModel

Ollama chat model integration.

Setup

Install langchain-ollama and download any models you want to use from ollama.

ollama pull gpt-oss:20b
pip install -U langchain-ollama

Key init args — completion params: model: str Name of Ollama model to use. reasoning: bool | None Controls the reasoning/thinking mode for supported models.

    - `True`: Enables reasoning mode. The model's reasoning process will be
        captured and returned separately in the `additional_kwargs` of the
        response message, under `reasoning_content`. The main response
        content will not include the reasoning tags.
    - `False`: Disables reasoning mode. The model will not perform any reasoning,
        and the response will not include any reasoning content.
    - `None` (Default): The model will use its default reasoning behavior. Note
        however, if the model's default behavior *is* to perform reasoning, think tags
        (`<think>` and `</think>`) will be present within the main response content
        unless you set `reasoning` to `True`.
temperature: float
    Sampling temperature. Ranges from `0.0` to `1.0`.
num_predict: int | None
    Max number of tokens to generate.

See full list of supported init args and their descriptions in the params section.

Instantiate

from langchain_ollama import ChatOllama

model = ChatOllama(
    model="gpt-oss:20b",
    validate_model_on_init=True,
    temperature=0.8,
    num_predict=256,
    # other params ...
)

Invoke

messages = [
    ("system", "You are a helpful translator. Translate the user sentence to French."),
    ("human", "I love programming."),
]
model.invoke(messages)

AIMessage(content='J'adore le programmation. (Note: "programming" can also refer to the act of writing code, so if you meant that, I could translate it as "J'adore programmer". But since you didn\'t specify, I assumed you were talking about the activity itself, which is what "le programmation" usually refers to.)', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:37:50.182604Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 3576619666, 'load_duration': 788524916, 'prompt_eval_count': 32, 'prompt_eval_duration': 128125000, 'eval_count': 71, 'eval_duration': 2656556000}, id='run-ba48f958-6402-41a5-b461-5e250a4ebd36-0')

Stream

for chunk in model.stream("Return the words Hello World!"):
    print(chunk.text, end="")

content='Hello' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
content=' World' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
content='!' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
content='' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:39:42.274449Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 411875125, 'load_duration': 1898166, 'prompt_eval_count': 14, 'prompt_eval_duration': 297320000, 'eval_count': 4, 'eval_duration': 111099000} id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'

stream = model.stream(messages)
full = next(stream)
for chunk in stream:
    full += chunk
full

AIMessageChunk(
    content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")',
    response_metadata={
        "model": "llama3",
        "created_at": "2024-07-04T03:38:54.933154Z",
        "message": {"role": "assistant", "content": ""},
        "done_reason": "stop",
        "done": True,
        "total_duration": 1977300042,
        "load_duration": 1345709,
        "prompt_eval_duration": 159343000,
        "eval_count": 47,
        "eval_duration": 1815123000,
    },
    id="run-3c81a3ed-3e79-4dd3-a796-04064d804890",
)

Async

await model.ainvoke("Hello how are you!")

AIMessage(
    content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?",
    response_metadata={
        "model": "llama3",
        "created_at": "2024-07-04T03:52:08.165478Z",
        "message": {"role": "assistant", "content": ""},
        "done_reason": "stop",
        "done": True,
        "total_duration": 2138492875,
        "load_duration": 1364000,
        "prompt_eval_count": 10,
        "prompt_eval_duration": 297081000,
        "eval_count": 47,
        "eval_duration": 1838524000,
    },
    id="run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0",
)

async for chunk in model.astream("Say hello world!"):
    print(chunk.content)

HEL
LO
WORLD
!

messages = [("human", "Say hello world!"), ("human", "Say goodbye world!")]
await model.abatch(messages)

[
    AIMessage(
        content="HELLO, WORLD!",
        response_metadata={
            "model": "llama3",
            "created_at": "2024-07-04T03:55:07.315396Z",
            "message": {"role": "assistant", "content": ""},
            "done_reason": "stop",
            "done": True,
            "total_duration": 1696745458,
            "load_duration": 1505000,
            "prompt_eval_count": 8,
            "prompt_eval_duration": 111627000,
            "eval_count": 6,
            "eval_duration": 185181000,
        },
        id="run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0",
    ),
    AIMessage(
        content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!",
        response_metadata={
            "model": "llama3",
            "created_at": "2024-07-04T03:55:07.018076Z",
            "message": {"role": "assistant", "content": ""},
            "done_reason": "stop",
            "done": True,
            "total_duration": 1399391083,
            "load_duration": 1187417,
            "prompt_eval_count": 20,
            "prompt_eval_duration": 230349000,
            "eval_count": 31,
            "eval_duration": 1166047000,
        },
        id="run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0",
    ),
]

JSON mode

json_model = ChatOllama(format="json")
json_model.invoke(
    "Return a query for the weather in a random location and time of day with two keys: location and time_of_day. "
    "Respond using JSON only."
).content

'{"location": "Pune, India", "time_of_day": "morning"}'

Tool Calling

from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field


class Multiply(BaseModel):
    a: int = Field(..., description="First integer")
    b: int = Field(..., description="Second integer")


ans = await chat.invoke("What is 45*67")
ans.tool_calls

[
    {
        "name": "Multiply",
        "args": {"a": 45, "b": 67},
        "id": "420c3f3b-df10-4188-945f-eb3abdb40622",
        "type": "tool_call",
    }
]

Thinking / Reasoning: You can enable reasoning mode for models that support it by setting the reasoning parameter to True in either the constructor or the invoke/stream methods. This will enable the model to think through the problem and return the reasoning process separately in the additional_kwargs of the response message, under reasoning_content.

If `reasoning` is set to `None`, the model will use its default reasoning
behavior, and any reasoning content will *not* be captured under the
`reasoning_content` key, but will be present within the main response content
as think tags (`<think>` and `</think>`).

!!! note
    This feature is only available for [models that support reasoning](https://ollama.com/search?c=thinking).

```python
from langchain_ollama import ChatOllama

model = ChatOllama(
    model="deepseek-r1:8b",
    validate_model_on_init=True,
    reasoning=True,
)

model.invoke("how many r in the word strawberry?")

# or, on an invocation basis:

model.invoke("how many r in the word strawberry?", reasoning=True)
# or model.stream("how many r in the word strawberry?", reasoning=True)

# If not provided, the invocation will default to the ChatOllama reasoning
# param provided (None by default).
```

```python
AIMessage(content='The word "strawberry" contains **three \'r\' letters**. Here\'s a breakdown for clarity:\n\n- The spelling of "strawberry" has two parts ... be 3.\n\nTo be thorough, let\'s confirm with an online source or common knowledge.\n\nI can recall that "strawberry" has: s-t-r-a-w-b-e-r-r-y — yes, three r\'s.\n\nPerhaps it\'s misspelled by some, but standard is correct.\n\nSo I think the response should be 3.\n'}, response_metadata={'model': 'deepseek-r1:8b', 'created_at': '2025-07-08T19:33:55.891269Z', 'done': True, 'done_reason': 'stop', 'total_duration': 98232561292, 'load_duration': 28036792, 'prompt_eval_count': 10, 'prompt_eval_duration': 40171834, 'eval_count': 3615, 'eval_duration': 98163832416, 'model_name': 'deepseek-r1:8b'}, id='run--18f8269f-6a35-4a7c-826d-b89d52c753b3-0', usage_metadata={'input_tokens': 10, 'output_tokens': 3615, 'total_tokens': 3625})

```

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

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

model `instance-attribute` ¶

model: str

Model name to use.

reasoning `class-attribute` `instance-attribute` ¶

reasoning: bool | str | None = None

Controls the reasoning/thinking mode for supported models.

True: Enables reasoning mode. The model's reasoning process will be captured and returned separately in the additional_kwargs of the response message, under reasoning_content. The main response content will not include the reasoning tags.
False: Disables reasoning mode. The model will not perform any reasoning, and the response will not include any reasoning content.
None (Default): The model will use its default reasoning behavior. Note however, if the model's default behavior is to perform reasoning, think tags (<think> and </think>) will be present within the main response content unless you set reasoning to True.
str: e.g. 'low', 'medium', 'high'. Enables reasoning with a custom intensity level. Currently, this is only supported gpt-oss. See the Ollama docs for more information.

validate_model_on_init `class-attribute` `instance-attribute` ¶

validate_model_on_init: bool = False

Whether to validate the model exists in Ollama locally on initialization.

Added in langchain-ollama 0.3.4

mirostat `class-attribute` `instance-attribute` ¶

mirostat: int | None = None

Enable Mirostat sampling for controlling perplexity.

(Default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)

mirostat_eta `class-attribute` `instance-attribute` ¶

mirostat_eta: float | None = None

Influences how quickly the algorithm responds to feedback from generated text.

A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive.

(Default: 0.1)

mirostat_tau `class-attribute` `instance-attribute` ¶

mirostat_tau: float | None = None

Controls the balance between coherence and diversity of the output.

A lower value will result in more focused and coherent text.

(Default: 5.0)

num_ctx `class-attribute` `instance-attribute` ¶

num_ctx: int | None = None

Sets the size of the context window used to generate the next token.

(Default: 2048)

num_gpu `class-attribute` `instance-attribute` ¶

num_gpu: int | None = None

The number of GPUs to use.

On macOS it defaults to 1 to enable metal support, 0 to disable.

num_thread `class-attribute` `instance-attribute` ¶

num_thread: int | None = None

Sets the number of threads to use during computation.

By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).

num_predict `class-attribute` `instance-attribute` ¶

num_predict: int | None = None

Maximum number of tokens to predict when generating text.

(Default: 128, -1 = infinite generation, -2 = fill context)

repeat_last_n `class-attribute` `instance-attribute` ¶

repeat_last_n: int | None = None

Sets how far back for the model to look back to prevent repetition.

(Default: 64, 0 = disabled, -1 = num_ctx)

repeat_penalty `class-attribute` `instance-attribute` ¶

repeat_penalty: float | None = None

Sets how strongly to penalize repetitions.

A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)

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

temperature: float | None = None

The temperature of the model.

Increasing the temperature will make the model answer more creatively.

(Default: 0.8)

seed `class-attribute` `instance-attribute` ¶

seed: int | None = None

Sets the random number seed to use for generation.

Setting this to a specific number will make the model generate the same text for the same prompt.

stop `class-attribute` `instance-attribute` ¶

stop: list[str] | None = None

Sets the stop tokens to use.

tfs_z `class-attribute` `instance-attribute` ¶

tfs_z: float | None = None

Tail free sampling.

Used to reduce the impact of less probable tokens from the output.

A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.

(Default: 1)

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

top_k: int | None = None

Reduces the probability of generating nonsense.

A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative.

(Default: 40)

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

top_p: float | None = None

Works together with top-k.

A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.

(Default: 0.9)

format `class-attribute` `instance-attribute` ¶

format: Literal['', 'json'] | JsonSchemaValue | None = None

Specify the format of the output (options: 'json', JSON schema).

keep_alive `class-attribute` `instance-attribute` ¶

keep_alive: int | str | None = None

How long the model will stay loaded into memory.

base_url `class-attribute` `instance-attribute` ¶

base_url: str | None = None

Base url the model is hosted under.

If none, defaults to the Ollama client default.

Supports userinfo auth in the format http://username:password@localhost:11434. Useful if your Ollama server is behind a proxy.

Warning

userinfo is not secure and should only be used for local testing or in secure environments. Avoid using it in production or over unsecured networks.

Note

If using userinfo, ensure that the Ollama server is configured to accept and validate these credentials.

Note

userinfo headers are passed to both sync and async clients.

client_kwargs `class-attribute` `instance-attribute` ¶

client_kwargs: dict | None = {}

Additional kwargs to pass to the httpx clients. Pass headers in here.

These arguments are passed to both synchronous and async clients.

Use sync_client_kwargs and async_client_kwargs to pass different arguments to synchronous and asynchronous clients.

async_client_kwargs `class-attribute` `instance-attribute` ¶

async_client_kwargs: dict | None = {}

Additional kwargs to merge with client_kwargs before passing to httpx client.

These are clients unique to the async client; for shared args use client_kwargs.

For a full list of the params, see the httpx documentation.

sync_client_kwargs `class-attribute` `instance-attribute` ¶

sync_client_kwargs: dict | None = {}

Additional kwargs to merge with client_kwargs before passing to httpx client.

These are clients unique to the sync client; for shared args use client_kwargs.

For a full list of the params, see the httpx documentation.

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")

init ¶

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

is_lc_serializable `classmethod` ¶

is_lc_serializable() -> bool

Is this class serializable?

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

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

get_lc_namespace `classmethod` ¶

get_lc_namespace() -> list[str]

Get the namespace of the LangChain object.

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

RETURNS	DESCRIPTION
`list[str]`	The namespace.

lc_id `classmethod` ¶

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

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

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

to_json ¶

to_json() -> SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

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

to_json_not_implemented ¶

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

RETURNS	DESCRIPTION
`SerializedNotImplemented`	`SerializedNotImplemented`.

configurable_fields ¶

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

Configure particular Runnable fields at runtime.

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

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

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

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:

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 = 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.

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:

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
`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:

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

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

dict ¶

dict(**kwargs: Any) -> dict

Return a dictionary of the LLM.

bind_tools ¶

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

Bind tool-like objects to this chat model.

Assumes model is compatible with OpenAI tool-calling API.

PARAMETER	DESCRIPTION
`tools`	A list of tool definitions to bind to this chat model. Supports any tool definition handled by `langchain_core.utils.function_calling.convert_to_openai_tool`. TYPE: `Sequence[dict[str, Any] \| type \| Callable \| BaseTool]`
`tool_choice`	If provided, which tool for model to call. This parameter is currently ignored as it is not supported by Ollama. TYPE: `dict \| str \| Literal['auto', 'any'] \| bool \| None` DEFAULT: `None`
`kwargs`	Any additional parameters are passed directly to `self.bind(kwargs)`. TYPE: `Any` DEFAULT:** `{}`

with_structured_output ¶

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

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

PARAMETER	DESCRIPTION
`schema`	The output schema. Can be passed in as: An OpenAI function/tool schema. A JSON Schema, A `TypedDict` class, Or a Pydantic class. If `schema` is a Pydantic class then the model output will be a Pydantic instance of that class, and the model-generated fields will be validated by the Pydantic class. Otherwise the model output will be a dict and will not be validated. See `langchain_core.utils.function_calling.convert_to_openai_tool` for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or `TypedDict` class. TYPE: `dict \| type`
`method`	The method for steering model generation, one of: `'json_schema'`: Uses Ollama's structured output API `'function_calling'`: Uses Ollama's tool-calling API `'json_mode'`: Specifies `format='json'`. Note that if using JSON mode then you must include instructions for formatting the output into the desired schema into the model call. TYPE: `Literal['function_calling', 'json_mode', 'json_schema']` DEFAULT: `'json_schema'`
`include_raw`	If `False` then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If `True` then both the raw model response (a `BaseMessage`) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a `dict` with keys `'raw'`, `'parsed'`, and `'parsing_error'`. TYPE: `bool` DEFAULT: `False`
`kwargs`	Additional keyword args aren't supported. TYPE: `Any` DEFAULT: `{}`

RETURNS DESCRIPTION

Runnable[LanguageModelInput, dict | BaseModel]

A Runnable that takes same inputs as a langchain_core.language_models.chat.BaseChatModel. If include_raw is False and schema is a Pydantic class, Runnable outputs an instance of schema (i.e., a Pydantic object). Otherwise, if include_raw is False then Runnable outputs a dict.

If include_raw is True, then Runnable outputs a dict with keys:

'raw': BaseMessage
'parsed': None if there was a parsing error, otherwise the type depends on the schema as described above.
'parsing_error': BaseException | None

Behavior changed in langchain-ollama 0.2.2

Added support for structured output API via format parameter.

Behavior changed in langchain-ollama 0.3.0

Updated default method to 'json_schema'.

Example: schema=Pydantic class, method='json_schema', include_raw=False

from typing import Optional

from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str | None = Field(
        default=...,
        description="A justification for the answer.",
    )


model = ChatOllama(model="llama3.1", temperature=0)
structured_model = model.with_structured_output(AnswerWithJustification)

structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")

# -> AnswerWithJustification(
#     answer='They weigh the same',
#     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )

Example: schema=Pydantic class, method='json_schema', include_raw=True

from langchain_ollama import ChatOllama
from pydantic import BaseModel


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str


model = ChatOllama(model="llama3.1", temperature=0)
structured_model = model.with_structured_output(
    AnswerWithJustification,
    include_raw=True,
)

structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
#     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
#     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
#     'parsing_error': None
# }

Example: schema=Pydantic class, method='function_calling', include_raw=False

from typing import Optional

from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str | None = Field(
        default=...,
        description="A justification for the answer.",
    )


model = ChatOllama(model="llama3.1", temperature=0)
structured_model = model.with_structured_output(
    AnswerWithJustification,
    method="function_calling",
)

structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")

# -> AnswerWithJustification(
#     answer='They weigh the same',
#     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )

Example: schema=TypedDict class, method='function_calling', include_raw=False

from typing_extensions import Annotated, TypedDict

from langchain_ollama import ChatOllama


class AnswerWithJustification(TypedDict):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: Annotated[str | None, None, "A justification for the answer."]


model = ChatOllama(model="llama3.1", temperature=0)
structured_model = model.with_structured_output(AnswerWithJustification)

structured_model.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
#     'answer': 'They weigh the same',
#     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }

Example: schema=OpenAI function schema, method='function_calling', include_raw=False

from langchain_ollama import ChatOllama

oai_schema = {
    'name': 'AnswerWithJustification',
    'description': 'An answer to the user question along with justification for the answer.',
    'parameters': {
        'type': 'object',
        'properties': {
            'answer': {'type': 'string'},
            'justification': {'description': 'A justification for the answer.', 'type': 'string'}
        },
        'required': ['answer']
    }

    model = ChatOllama(model="llama3.1", temperature=0)
    structured_model = model.with_structured_output(oai_schema)

    structured_model.invoke(
        "What weighs more a pound of bricks or a pound of feathers"
    )
    # -> {
    #     'answer': 'They weigh the same',
    #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
    # }

Example: schema=Pydantic class, method='json_mode', include_raw=True

from langchain_ollama import ChatOllama
from pydantic import BaseModel


class AnswerWithJustification(BaseModel):
    answer: str
    justification: str


model = ChatOllama(model="llama3.1", temperature=0)
structured_model = model.with_structured_output(
    AnswerWithJustification, method="json_mode", include_raw=True
)

structured_model.invoke(
    "Answer the following question. "
    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
    "What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
#     'raw': AIMessage(content='{\\n    "answer": "They are both the same weight.",\\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
#     'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
#     'parsing_error': None
# }

OllamaEmbeddings ¶

Bases: BaseModel, Embeddings

Ollama embedding model integration.

Set up a local Ollama instance

Install the Ollama package and set up a local Ollama instance.

You will need to choose a model to serve.

You can view a list of available models via the model library.

To fetch a model from the Ollama model library use ollama pull <name-of-model>.

For example, to pull the llama3 model:

ollama pull llama3

This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.

On Mac, the models will be downloaded to ~/.ollama/models
On Linux (or WSL), the models will be stored at /usr/share/ollama/.ollama/models

You can specify the exact version of the model of interest as such ollama pull vicuna:13b-v1.5-16k-q4_0.

To view pulled models:

ollama list

To start serving:

ollama serve

View the Ollama documentation for more commands.

ollama help

Install the langchain-ollama integration package:

pip install -U langchain_ollama

Key init args — completion params: model: str Name of Ollama model to use. base_url: str | None Base url the model is hosted under.

See full list of supported init args and their descriptions in the params section.

Instantiate

from langchain_ollama import OllamaEmbeddings

embed = OllamaEmbeddings(model="llama3")

Embed single text

input_text = "The meaning of life is 42"
vector = embed.embed_query(input_text)
print(vector[:3])

[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Embed multiple texts

input_texts = ["Document 1...", "Document 2..."]
vectors = embed.embed_documents(input_texts)
print(len(vectors))
# The first 3 coordinates for the first vector
print(vectors[0][:3])

2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Async

vector = await embed.aembed_query(input_text)
print(vector[:3])

# multiple:
# await embed.aembed_documents(input_texts)

[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]

METHOD	DESCRIPTION
`embed_documents`	Embed search docs.
`embed_query`	Embed query text.
`aembed_documents`	Embed search docs.
`aembed_query`	Embed query text.

model `instance-attribute` ¶

model: str

Model name to use.

validate_model_on_init `class-attribute` `instance-attribute` ¶

validate_model_on_init: bool = False

Whether to validate the model exists in ollama locally on initialization.

Added in langchain-ollama 0.3.4

base_url `class-attribute` `instance-attribute` ¶

base_url: str | None = None

Base url the model is hosted under.

If none, defaults to the Ollama client default.

Supports userinfo auth in the format http://username:password@localhost:11434. Useful if your Ollama server is behind a proxy.

Warning

userinfo is not secure and should only be used for local testing or in secure environments. Avoid using it in production or over unsecured networks.

Note

If using userinfo, ensure that the Ollama server is configured to accept and validate these credentials.

Note

userinfo headers are passed to both sync and async clients.

client_kwargs `class-attribute` `instance-attribute` ¶

client_kwargs: dict | None = {}

Additional kwargs to pass to the httpx clients. Pass headers in here.

These arguments are passed to both synchronous and async clients.

Use sync_client_kwargs and async_client_kwargs to pass different arguments to synchronous and asynchronous clients.

async_client_kwargs `class-attribute` `instance-attribute` ¶

async_client_kwargs: dict | None = {}

Additional kwargs to merge with client_kwargs before passing to httpx client.

These are clients unique to the async client; for shared args use client_kwargs.

For a full list of the params, see the httpx documentation.

sync_client_kwargs `class-attribute` `instance-attribute` ¶

sync_client_kwargs: dict | None = {}

Additional kwargs to merge with client_kwargs before passing to httpx client.

These are clients unique to the sync client; for shared args use client_kwargs.

For a full list of the params, see the httpx documentation.

mirostat `class-attribute` `instance-attribute` ¶

mirostat: int | None = None

Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)

mirostat_eta `class-attribute` `instance-attribute` ¶

mirostat_eta: float | None = None

Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)

mirostat_tau `class-attribute` `instance-attribute` ¶

mirostat_tau: float | None = None

Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)

num_ctx `class-attribute` `instance-attribute` ¶

num_ctx: int | None = None

Sets the size of the context window used to generate the next token. (Default: 2048)

num_gpu `class-attribute` `instance-attribute` ¶

num_gpu: int | None = None

The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable.

keep_alive `class-attribute` `instance-attribute` ¶

keep_alive: int | None = None

Controls how long the model will stay loaded into memory following the request (default: 5m)

num_thread `class-attribute` `instance-attribute` ¶

num_thread: int | None = None

Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).

repeat_last_n `class-attribute` `instance-attribute` ¶

repeat_last_n: int | None = None

Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)

repeat_penalty `class-attribute` `instance-attribute` ¶

repeat_penalty: float | None = None

Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)

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

temperature: float | None = None

The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)

stop `class-attribute` `instance-attribute` ¶

stop: list[str] | None = None

Sets the stop tokens to use.

tfs_z `class-attribute` `instance-attribute` ¶

tfs_z: float | None = None

Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)

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

top_k: int | None = None

Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)

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

top_p: float | None = None

Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)

embed_documents ¶

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

Embed search docs.

embed_query ¶

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

Embed query text.

aembed_documents `async` ¶

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

Embed search docs.

aembed_query `async` ¶

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

Embed query text.

OllamaLLM ¶

Bases: BaseLLM

Ollama large language models.

Setup

Install langchain-ollama and install/run the Ollama server locally:

pip install -U langchain-ollama
# Visit https://ollama.com/download to download and install Ollama
# (Linux users): start the server with `ollama serve`

Download a model to use:

ollama pull llama3.1

Key init args — generation params: model: str Name of the Ollama model to use (e.g. 'llama4'). temperature: float | None Sampling temperature. Higher values make output more creative. num_predict: int | None Maximum number of tokens to predict. top_k: int | None Limits the next token selection to the K most probable tokens. top_p: float | None Nucleus sampling parameter. Higher values lead to more diverse text. mirostat: int | None Enable Mirostat sampling for controlling perplexity. seed: int | None Random number seed for generation reproducibility.

Key init args — client params: base_url: Base URL where Ollama server is hosted. keep_alive: How long the model stays loaded into memory. format: Specify the format of the output.

See full list of supported init args and their descriptions in the params section.

Instantiate

from langchain_ollama import OllamaLLM

model = OllamaLLM(
    model="llama3.1",
    temperature=0.7,
    num_predict=256,
    # base_url="http://localhost:11434",
    # other params...
)

Invoke

input_text = "The meaning of life is "
response = model.invoke(input_text)
print(response)

"a philosophical question that has been contemplated by humans for
centuries..."

Stream

for chunk in model.stream(input_text):
    print(chunk, end="")

a philosophical question that has been contemplated by humans for
centuries...

Async

response = await model.ainvoke(input_text)

# stream:
# async for chunk in model.astream(input_text):
#     print(chunk, end="")

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

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.

model `instance-attribute` ¶

model: str

Model name to use.

reasoning `class-attribute` `instance-attribute` ¶

reasoning: bool | None = None

Controls the reasoning/thinking mode for supported models.

True: Enables reasoning mode. The model's reasoning process will be captured and returned separately in the additional_kwargs of the response message, under reasoning_content. The main response content will not include the reasoning tags.
False: Disables reasoning mode. The model will not perform any reasoning, and the response will not include any reasoning content.
None (Default): The model will use its default reasoning behavior. If the model performs reasoning, the <think> and </think> tags will be present directly within the main response content.

validate_model_on_init `class-attribute` `instance-attribute` ¶

validate_model_on_init: bool = False

Whether to validate the model exists in ollama locally on initialization.

Added in langchain-ollama 0.3.4

mirostat `class-attribute` `instance-attribute` ¶

mirostat: int | None = None

Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)

mirostat_eta `class-attribute` `instance-attribute` ¶

mirostat_eta: float | None = None

Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)

mirostat_tau `class-attribute` `instance-attribute` ¶

mirostat_tau: float | None = None

Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)

num_ctx `class-attribute` `instance-attribute` ¶

num_ctx: int | None = None

Sets the size of the context window used to generate the next token. (Default: 2048)

num_gpu `class-attribute` `instance-attribute` ¶

num_gpu: int | None = None

The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable.

num_thread `class-attribute` `instance-attribute` ¶

num_thread: int | None = None

Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).

num_predict `class-attribute` `instance-attribute` ¶

num_predict: int | None = None

Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)

repeat_last_n `class-attribute` `instance-attribute` ¶

repeat_last_n: int | None = None

Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)

repeat_penalty `class-attribute` `instance-attribute` ¶

repeat_penalty: float | None = None

Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)

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

temperature: float | None = None

The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)

seed `class-attribute` `instance-attribute` ¶

seed: int | None = None

Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt.

stop `class-attribute` `instance-attribute` ¶

stop: list[str] | None = None

Sets the stop tokens to use.

tfs_z `class-attribute` `instance-attribute` ¶

tfs_z: float | None = None

Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)

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

top_k: int | None = None

Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)

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

top_p: float | None = None

Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)

format `class-attribute` `instance-attribute` ¶

format: Literal['', 'json'] = ''

Specify the format of the output (options: 'json')

keep_alive `class-attribute` `instance-attribute` ¶

keep_alive: int | str | None = None

How long the model will stay loaded into memory.

base_url `class-attribute` `instance-attribute` ¶

base_url: str | None = None

Base url the model is hosted under.

If none, defaults to the Ollama client default.

Supports userinfo auth in the format http://username:password@localhost:11434. Useful if your Ollama server is behind a proxy.

Warning

userinfo is not secure and should only be used for local testing or in secure environments. Avoid using it in production or over unsecured networks.

Note

If using userinfo, ensure that the Ollama server is configured to accept and validate these credentials.

Note

userinfo headers are passed to both sync and async clients.

client_kwargs `class-attribute` `instance-attribute` ¶

client_kwargs: dict | None = {}

Additional kwargs to pass to the httpx clients. Pass headers in here.

These arguments are passed to both synchronous and async clients.

Use sync_client_kwargs and async_client_kwargs to pass different arguments to synchronous and asynchronous clients.

async_client_kwargs `class-attribute` `instance-attribute` ¶

async_client_kwargs: dict | None = {}

Additional kwargs to merge with client_kwargs before passing to httpx client.

These are clients unique to the async client; for shared args use client_kwargs.

For a full list of the params, see the httpx documentation.

sync_client_kwargs `class-attribute` `instance-attribute` ¶

sync_client_kwargs: dict | None = {}

Additional kwargs to merge with client_kwargs before passing to httpx client.

These are clients unique to the sync client; for shared args use client_kwargs.

For a full list of the params, see the httpx documentation.

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")

init ¶

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

is_lc_serializable `classmethod` ¶

is_lc_serializable() -> bool

Is this class serializable?

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

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

get_lc_namespace `classmethod` ¶

get_lc_namespace() -> list[str]

Get the namespace of the LangChain object.

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

RETURNS	DESCRIPTION
`list[str]`	The namespace.

lc_id `classmethod` ¶

lc_id() -> list[str]

Return a unique identifier for this class for serialization purposes.

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

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

to_json ¶

to_json() -> SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

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

to_json_not_implemented ¶

to_json_not_implemented() -> SerializedNotImplemented

Serialize a "not implemented" object.

RETURNS	DESCRIPTION
`SerializedNotImplemented`	`SerializedNotImplemented`.

configurable_fields ¶

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

Configure particular Runnable fields at runtime.

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

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

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

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:

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 ¶

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:

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")

langchain-ollama¶

langchain_ollama ¶

ChatOllama ¶

name class-attribute instance-attribute ¶

InputType property ¶

OutputType property ¶

input_schema property ¶

output_schema property ¶

config_specs property ¶

lc_secrets 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 ¶

rate_limiter class-attribute instance-attribute ¶

disable_streaming class-attribute instance-attribute ¶

output_version class-attribute instance-attribute ¶

profile class-attribute instance-attribute ¶

model instance-attribute ¶

reasoning class-attribute instance-attribute ¶

validate_model_on_init class-attribute instance-attribute ¶

mirostat class-attribute instance-attribute ¶

mirostat_eta class-attribute instance-attribute ¶

mirostat_tau class-attribute instance-attribute ¶

num_ctx class-attribute instance-attribute ¶

num_gpu class-attribute instance-attribute ¶

num_thread class-attribute instance-attribute ¶

num_predict class-attribute instance-attribute ¶

repeat_last_n class-attribute instance-attribute ¶

repeat_penalty class-attribute instance-attribute ¶

temperature class-attribute instance-attribute ¶

seed class-attribute instance-attribute ¶

stop class-attribute instance-attribute ¶

tfs_z class-attribute instance-attribute ¶

top_k class-attribute instance-attribute ¶

top_p class-attribute instance-attribute ¶

format class-attribute instance-attribute ¶

keep_alive class-attribute instance-attribute ¶

base_url class-attribute instance-attribute ¶

client_kwargs class-attribute instance-attribute ¶

async_client_kwargs class-attribute instance-attribute ¶

sync_client_kwargs class-attribute instance-attribute ¶

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 ¶

`langchain-ollama`¶

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

InputType `property` ¶

OutputType `property` ¶

input_schema `property` ¶

output_schema `property` ¶

config_specs `property` ¶

lc_secrets `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` ¶

rate_limiter `class-attribute` `instance-attribute` ¶

disable_streaming `class-attribute` `instance-attribute` ¶

output_version `class-attribute` `instance-attribute` ¶

profile `class-attribute` `instance-attribute` ¶

model `instance-attribute` ¶

reasoning `class-attribute` `instance-attribute` ¶

validate_model_on_init `class-attribute` `instance-attribute` ¶

mirostat `class-attribute` `instance-attribute` ¶

mirostat_eta `class-attribute` `instance-attribute` ¶

mirostat_tau `class-attribute` `instance-attribute` ¶

num_ctx `class-attribute` `instance-attribute` ¶

num_gpu `class-attribute` `instance-attribute` ¶

num_thread `class-attribute` `instance-attribute` ¶

num_predict `class-attribute` `instance-attribute` ¶

repeat_last_n `class-attribute` `instance-attribute` ¶

repeat_penalty `class-attribute` `instance-attribute` ¶

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

seed `class-attribute` `instance-attribute` ¶

stop `class-attribute` `instance-attribute` ¶

tfs_z `class-attribute` `instance-attribute` ¶

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

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

format `class-attribute` `instance-attribute` ¶

keep_alive `class-attribute` `instance-attribute` ¶

base_url `class-attribute` `instance-attribute` ¶

client_kwargs `class-attribute` `instance-attribute` ¶

async_client_kwargs `class-attribute` `instance-attribute` ¶

sync_client_kwargs `class-attribute` `instance-attribute` ¶

or ¶

ror ¶

ainvoke `async` ¶

abatch `async` ¶

abatch_as_completed `async` ¶

astream `async` ¶

astream_log `async` ¶

astream_events `async` ¶

atransform `async` ¶

init ¶

is_lc_serializable `classmethod` ¶

get_lc_namespace `classmethod` ¶

lc_id `classmethod` ¶

agenerate_prompt `async` ¶

agenerate `async` ¶

model `instance-attribute` ¶

validate_model_on_init `class-attribute` `instance-attribute` ¶

base_url `class-attribute` `instance-attribute` ¶

client_kwargs `class-attribute` `instance-attribute` ¶

async_client_kwargs `class-attribute` `instance-attribute` ¶

sync_client_kwargs `class-attribute` `instance-attribute` ¶

mirostat `class-attribute` `instance-attribute` ¶

mirostat_eta `class-attribute` `instance-attribute` ¶

mirostat_tau `class-attribute` `instance-attribute` ¶

num_ctx `class-attribute` `instance-attribute` ¶

num_gpu `class-attribute` `instance-attribute` ¶

keep_alive `class-attribute` `instance-attribute` ¶

num_thread `class-attribute` `instance-attribute` ¶

repeat_last_n `class-attribute` `instance-attribute` ¶

repeat_penalty `class-attribute` `instance-attribute` ¶

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

stop `class-attribute` `instance-attribute` ¶

tfs_z `class-attribute` `instance-attribute` ¶

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

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

aembed_documents `async` ¶

aembed_query `async` ¶