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Full config

Configurable

Bases: TypedDict

The raw config that is passed to langgraph client.

Note: ONLY use primitive types otherwise they don't show up in langgraph studio Note: This should not be used directly by nodes. They should use FullConfig.from_run_config(config)

assistant_name instance-attribute

assistant_type instance-attribute

assistant_version instance-attribute

conversation_uid instance-attribute

conversation_version instance-attribute

running_as_subgraph instance-attribute

use_latest_assistant instance-attribute

user_id instance-attribute

FullConfig

Bases: BaseModel

Config that is available to nodes at runtime.

Generally consider to be fixed for the duration of a thread, although technically new config can be passed when resuming.

Note: This validated version adds the dependency injections and additional methods for easier access to complex config data.

This is what nodes should use

assistant_config_id instance-attribute

di instance-attribute

main_conv_id instance-attribute

model_config = ConfigDict(extra='ignore', arbitrary_types_allowed=True) class-attribute instance-attribute

running_as_subgraph = False class-attribute instance-attribute

user_pk_id instance-attribute

from_run_config(config) classmethod

Ensure that required config exists, and add the dependency injections.

Note: The Configurable type is not enforced at runtime, so have to do all validation here. Note: This must be re-run in every node. This is also necessary in-case the graph is resumed.

get_agent() async

get_assistant() async

get_chat_model() async

get_options() async

get_sub_assistant_infos() async

get_user() async

RequiresConfigError

Bases: LanggraphError

Error raised when a node requires a config but it is not available.

SubAssistantInfo dataclass

description instance-attribute

name instance-attribute

__init__(name, description)

configurable_from_values(assistant, user_id, conversation_id, use_latest_assistant=True)

configurable_to_runnable_config(configurable, recursion_limit)