nip.parameters.agents.PureTextAgentParameters

class nip.parameters.agents.PureTextAgentParameters(agent_lr_factor: ~nip.parameters.agents.LrFactors | dict | None = None, body_lr_factor: ~nip.parameters.agents.LrFactors | dict | None = None, update_schedule: ~nip.parameters.update_schedule.AgentUpdateSchedule = ConstantUpdateSchedule(), use_manual_architecture: bool = False, normalize_message_history: bool = False, load_checkpoint_and_parameters: bool = False, checkpoint_entity: str = <factory>, checkpoint_project: str = <factory>, checkpoint_run_id: str | None = None, checkpoint_version: str = 'latest', use_orthogonal_initialisation: bool = True, orthogonal_initialisation_gain: float = 1.0, model_provider: ~typing.Literal['OpenAI'] = 'OpenAI', model_name: str = 'gpt-4o-mini-2024-07-18', use_dummy_api: bool = False, shared_model_group: str | None = None, temperature: float | None = None, top_p: float | None = None, fine_tune_from_scratch: bool = True, freeze_agent: bool = False, max_response_words: int = 150, max_tokens_per_message: int | None = None, num_invalid_generation_retries: int = 20)

Additional parameters for text-based agents who use APIs to generate responses.

Parameters:

• model_provider (Literal["OpenAI"]) – The provider of the model and API to use.

• model_name (str) – The name of the model to use.

• use_dummy_api (bool) – Whether to use a dummy API instead of the real API. This is useful for testing the agent without making real API requests.

• shared_model_group (str | None) – The group of agents which share the same model. When two agents share this value, they will use the same model inference. For fine-tuning, this model is trained on a copy of the rollouts and rewards for each agent in the group. When this is None, the agent is in a group whose name is the same as the agent’s name.

• temperature (float | None) – The temperature to use when sampling from the model. If None, the model uses the default temperature. Only one of temperature and top_p should be set.

• top_p (float | None) – The top-p value to use when sampling from the model. A value 0.1 means only the top 10% of tokens are considered when sampling. If None, the model uses the default top-p value. Only one of temperature and top_p should be set.

• fine_tune_from_scratch (bool) – Whether to fine-tune the model from scratch each iteration, or continue fine-tuning from the previous iteration.

• freeze_agent (bool) – Whether to freeze the agent (i.e. not fine-tune it).

• max_response_words (int) – In the system prompt, we say that the agent should respond with a message of at most this many words.

• max_tokens_per_message (int | None) – The maximum number of tokens which the model is allowed to generate in a single message. If None, this is calculated based on the max_response_words.

• num_invalid_generation_retries (int) – The number of times to retry generating a message if the model returns an invalid response.

Methods Summary

__eq__(other)	Return self==value.
__init__([agent_lr_factor, body_lr_factor, ...])
__post_init__()
__repr__()	Return repr(self).
_get_param_class_from_dict(param_dict)	Try to get the parameter class from a dictionary of serialised parameters.
construct_test_params()	Construct test parameters for the agent.
from_dict(params_dict[, ignore_extra_keys])	Create a parameters object from a dictionary.
get(address)	Get a value from the parameters object using a dot-separated address.
load_from_wandb_config(wandb_config)	Load the parameters from a W&B config dictionary.
to_dict()	Convert the parameters object to a dictionary.

Attributes

LOAD_PRESERVED_PARAMETERS
agent_lr_factor
body_lr_factor
checkpoint_run_id
checkpoint_version
fine_tune_from_scratch
freeze_agent
is_random
load_checkpoint_and_parameters
max_response_words
max_tokens_per_message
model_name
model_provider
normalize_message_history
num_invalid_generation_retries
orthogonal_initialisation_gain
shared_model_group
temperature
top_p
update_schedule
use_dummy_api
use_manual_architecture
use_orthogonal_initialisation
checkpoint_entity
checkpoint_project

Methods

__eq__(other)

Return self==value.

__init__(agent_lr_factor: ~nip.parameters.agents.LrFactors | dict | None = None, body_lr_factor: ~nip.parameters.agents.LrFactors | dict | None = None, update_schedule: ~nip.parameters.update_schedule.AgentUpdateSchedule = ConstantUpdateSchedule(), use_manual_architecture: bool = False, normalize_message_history: bool = False, load_checkpoint_and_parameters: bool = False, checkpoint_entity: str = <factory>, checkpoint_project: str = <factory>, checkpoint_run_id: str | None = None, checkpoint_version: str = 'latest', use_orthogonal_initialisation: bool = True, orthogonal_initialisation_gain: float = 1.0, model_provider: ~typing.Literal['OpenAI'] = 'OpenAI', model_name: str = 'gpt-4o-mini-2024-07-18', use_dummy_api: bool = False, shared_model_group: str | None = None, temperature: float | None = None, top_p: float | None = None, fine_tune_from_scratch: bool = True, freeze_agent: bool = False, max_response_words: int = 150, max_tokens_per_message: int | None = None, num_invalid_generation_retries: int = 20) → None

__post_init__()

__repr__()

Return repr(self).

classmethod _get_param_class_from_dict(param_dict: dict) → type[ParameterValue] | None

Try to get the parameter class from a dictionary of serialised parameters.

Parameters:

param_dict (dict) – A dictionary of parameters, which may have come from a to_dict method. This dictionary may contain a _type key, which is used to determine the class of the parameter.

Returns:

param_class (type[ParameterValue] | None) – The class of the parameter, if it can be determined.

Raises:

ValueError – If the class specified in the dictionary is not a valid parameter class.

classmethod construct_test_params() → PureTextAgentParameters

Construct test parameters for the agent.

For this agent, we use the dummy API, so that we don’t need to make real API requests.

Returns:

test_params (PureTextAgentParameters) – The test parameters.

classmethod from_dict(params_dict: dict, ignore_extra_keys: bool = False) → AgentsParameters

Create a parameters object from a dictionary.

Parameters:

• params_dict (dict) – A dictionary of the parameters.

• ignore_extra_keys (bool, default=False) – If True, ignore keys in the dictionary that do not correspond to fields in the parameters object.

Returns:

hyper_params (AgentsParameters) – The parameters object.

get(address: str) → Any

Get a value from the parameters object using a dot-separated address.

Parameters:

address (str) – The path to the value in the parameters object, separated by dots.

Returns:

value (Any) – The value at the address.

Raises:

KeyError – If the address does not exist.

load_from_wandb_config(wandb_config: dict)

Load the parameters from a W&B config dictionary.

Parameters:

wandb_config (dict) – The W&B config dictionary for this agent (e.g. wandb_run.config["agents"][agent_name]).

to_dict() → dict

Convert the parameters object to a dictionary.

Adds the is_random parameter to the dictionary. This is not a field of the parameters object, but we want to include it in the dictionary for logging.

Returns:

params_dict (dict) – A dictionary of the parameters.