`langchain_core.runnables.history`.RunnableWithMessageHistory¶

class langchain_core.runnables.history.RunnableWithMessageHistory[source]¶

Bases: RunnableBindingBase

A runnable that manages chat message history for another runnable.

Base runnable must have inputs and outputs that can be converted to a list of BaseMessages.

RunnableWithMessageHistory must always be called with a config that contains session_id, e.g. ``{“configurable”: {“session_id”: “<SESSION_ID>”}}`.

Example (dict input):

from typing import Optional

from langchain_community.chat_models import ChatAnthropic
from langchain_community.chat_message_histories import RedisChatMessageHistory

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory


prompt = ChatPromptTemplate.from_messages([
    ("system", "You're an assistant who's good at {ability}"),
    MessagesPlaceholder(variable_name="history"),
    ("human", "{question}"),
])

chain = prompt | ChatAnthropic(model="claude-2")

chain_with_history = RunnableWithMessageHistory(
    chain,
    RedisChatMessageHistory,
    input_messages_key="question",
    history_messages_key="history",
)

chain_with_history.invoke(
    {"ability": "math", "question": "What does cosine mean?"},
    config={"configurable": {"session_id": "foo"}}
)
# -> "Cosine is ..."
chain_with_history.invoke(
    {"ability": "math", "question": "What's its inverse"},
    config={"configurable": {"session_id": "foo"}}
)
# -> "The inverse of cosine is called arccosine ..."

Example (get_session_history takes two keys, user_id and conversation id):

store = {}

def get_session_history(
    user_id: str, conversation_id: str
) -> ChatMessageHistory:
    if (user_id, conversation_id) not in store:
        store[(user_id, conversation_id)] = ChatMessageHistory()
    return store[(user_id, conversation_id)]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You're an assistant who's good at {ability}"),
    MessagesPlaceholder(variable_name="history"),
    ("human", "{question}"),
])

chain = prompt | ChatAnthropic(model="claude-2")

with_message_history = RunnableWithMessageHistory(
    chain,
    get_session_history=get_session_history,
    input_messages_key="messages",
    history_messages_key="history",
    history_factory_config=[
        ConfigurableFieldSpec(
            id="user_id",
            annotation=str,
            name="User ID",
            description="Unique identifier for the user.",
            default="",
            is_shared=True,
        ),
        ConfigurableFieldSpec(
            id="conversation_id",
            annotation=str,
            name="Conversation ID",
            description="Unique identifier for the conversation.",
            default="",
            is_shared=True,
        ),
    ],
)

chain_with_history.invoke(
    {"ability": "math", "question": "What does cosine mean?"},
    config={"configurable": {"user_id": "123", "conversation_id": "1"}}
)

Initialize RunnableWithMessageHistory.

Parameters

runnable –
The base Runnable to be wrapped. Must take as input one of: 1. A sequence of BaseMessages 2. A dict with one key for all messages 3. A dict with one key for the current input string/message(s) and

a separate key for historical messages. If the input key points to a string, it will be treated as a HumanMessage in history.

Must return as output one of: 1. A string which can be treated as an AIMessage 2. A BaseMessage or sequence of BaseMessages 3. A dict with a key for a BaseMessage or sequence of BaseMessages
get_session_history –
Function that returns a new BaseChatMessageHistory. This function should either take a single positional argument session_id of type string and return a corresponding chat message history instance. .. code-block:: python

def get_session_history(
session_id: str, *, user_id: Optional[str]=None

) -> BaseChatMessageHistory:
…

Or it should take keyword arguments that match the keys of session_history_config_specs and return a corresponding chat message history instance.
```
def get_session_history(
    *,
    user_id: str,
    thread_id: str,
) -> BaseChatMessageHistory:
    ...
```
input_messages_key – Must be specified if the base runnable accepts a dict as input.
output_messages_key – Must be specified if the base runnable returns a dict as output.
history_messages_key – Must be specified if the base runnable accepts a dict as input and expects a separate key for historical messages.
history_factory_config – Configure fields that should be passed to the chat history factory. See ConfigurableFieldSpec for more details. Specifying these allows you to pass multiple config keys into the get_session_history factory.
**kwargs – Arbitrary additional kwargs to pass to parent class RunnableBindingBase init.

param bound: Runnable[Input, Output] [Required]¶: The underlying runnable that this runnable delegates to.

param config: RunnableConfig [Optional]¶: The config to bind to the underlying runnable.

param config_factories: List[Callable[[RunnableConfig], RunnableConfig]] [Optional]¶: The config factories to bind to the underlying runnable.

param custom_input_type: Optional[Any] = None¶

Override the input type of the underlying runnable with a custom type.

The type can be a pydantic model, or a type annotation (e.g., List[str]).

param custom_output_type: Optional[Any] = None¶

Override the output type of the underlying runnable with a custom type.

The type can be a pydantic model, or a type annotation (e.g., List[str]).

param get_session_history: GetSessionHistoryCallable [Required]¶

param history_factory_config: Sequence[ConfigurableFieldSpec] [Required]¶

param history_messages_key: Optional[str] = None¶

param input_messages_key: Optional[str] = None¶

param kwargs: Mapping[str, Any] [Optional]¶

kwargs to pass to the underlying runnable when running.

For example, when the runnable binding is invoked the underlying runnable will be invoked with the same input but with these additional kwargs.

param output_messages_key: Optional[str] = None¶

async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶

Default implementation runs ainvoke in parallel using asyncio.gather.

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

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

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

Default implementation of ainvoke, calls invoke from a thread.

The default implementation allows usage of async code even if the runnable did not implement a native async version of invoke.

Subclasses should override this method if they can run asynchronously.

assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) → RunnableSerializable[Any, Any]¶: Assigns new fields to the dict output of this runnable. Returns a new runnable.

async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶: Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶

Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc.

Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.

The jsonpatch ops can be applied in order to construct state.

Parameters

input – The input to the runnable.
config – The config to use for the runnable.
diff – Whether to yield diffs between each step, or the current state.
with_streamed_output_list – Whether to yield the streamed_output list.
include_names – Only include logs with these names.
include_types – Only include logs with these types.
include_tags – Only include logs with these tags.
exclude_names – Exclude logs with these names.
exclude_types – Exclude logs with these types.
exclude_tags – Exclude logs with these tags.

async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Any) → AsyncIterator[Output]¶: Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if they can start producing output while input is still being generated.

batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶

Default implementation runs invoke in parallel using a thread pool executor.

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

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

bind(**kwargs: Any) → Runnable[Input, Output]¶: Bind arguments to a Runnable, returning a new Runnable.

config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶

The type of config this runnable accepts specified as a pydantic model.

To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.

Parameters: include – A list of fields to include in the config schema.
Returns: A pydantic model that can be used to validate config.

configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶

configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_graph(config: Optional[RunnableConfig] = None) → Graph¶: Return a graph representation of this runnable.

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

Get a pydantic model that can be used to validate input to the runnable.

Runnables 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.

Parameters: config – A config to use when generating the schema.
Returns: A pydantic model that can be used to validate input.

classmethod get_lc_namespace() → List[str][source]¶: Get the namespace of the langchain object.

get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶: Get the name of the runnable.

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

Get a pydantic model that can be used to validate output to the runnable.

Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with.

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

Parameters: config – A config to use when generating the schema.
Returns: A pydantic model that can be used to validate output.

get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶

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

Transform a single input into an output. Override to implement.

Parameters

input – The input to the runnable.
config – A config to use when invoking the runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details.

Returns

The output of the runnable.

classmethod is_lc_serializable() → bool¶: Is this class serializable?

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod lc_id() → List[str]¶

A unique identifier for this class for serialization purposes.

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

map() → Runnable[List[Input], List[Output]]¶: Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

pick(keys: Union[str, List[str]]) → RunnableSerializable[Any, Any]¶: Pick keys from the dict output of this runnable. Returns a new runnable.

pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]¶: Compose this runnable with another object to create a RunnableSequence.

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶: Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶

to_json_not_implemented() → SerializedNotImplemented¶

transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Any) → Iterator[Output]¶: Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶: Bind config to a Runnable, returning a new Runnable.

with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶

Add fallbacks to a runnable, returning a new Runnable.

Parameters

fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.

Returns

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

with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶

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

on_start: Called before the runnable starts running, with the Run object. on_end: Called after the runnable finishes running, with the Run object. on_error: Called if the runnable throws an error, with the Run object.

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

with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶

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

Parameters

retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time between retries
stop_after_attempt – The maximum number of attempts to make before giving up

Returns

A new Runnable that retries the original runnable on exceptions.

with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶: Bind input and output types to a Runnable, returning a new Runnable.

property InputType: Type[langchain_core.runnables.utils.Input]¶: The type of input this runnable accepts specified as a type annotation.

property OutputType: Type[langchain_core.runnables.utils.Output]¶: The type of output this runnable produces specified as a type annotation.

property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶: List configurable fields for this runnable.

property input_schema: Type[pydantic.main.BaseModel]¶: The type of input this runnable accepts specified as a pydantic model.

property lc_attributes: Dict¶

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

These attributes must be accepted by the constructor.

property lc_secrets: Dict[str, str]¶

A map of constructor argument names to secret ids.

For example,: {“openai_api_key”: “OPENAI_API_KEY”}

name: Optional[str] = None¶: The name of the runnable. Used for debugging and tracing.

property output_schema: Type[pydantic.main.BaseModel]¶: The type of output this runnable produces specified as a pydantic model.

langchain_core.runnables.history.RunnableWithMessageHistory¶

`langchain_core.runnables.history`.RunnableWithMessageHistory¶