langchain_experimental.generative_agents.generative_agent.GenerativeAgent

class langchain_experimental.generative_agents.generative_agent.GenerativeAgent

Bases: BaseModel

An Agent as a character with memory and innate characteristics.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param age: Optional[int] = None

The optional age of the character.

param daily_summaries: List[str] [Optional]

Summary of the events in the plan that the agent took.

param last_refreshed: datetime.datetime [Optional]

The last time the character’s summary was regenerated.

param llm: langchain_core.language_models.base.BaseLanguageModel [Required]

The underlying language model.

param memory: langchain_experimental.generative_agents.memory.GenerativeAgentMemory [Required]

The memory object that combines relevance, recency, and ‘importance’.

param name: str [Required]

The character’s name.

param status: str [Required]

The traits of the character you wish not to change.

param summary: str = ''

Stateful self-summary generated via reflection on the character’s memory.

param summary_refresh_seconds: int = 3600

How frequently to re-generate the summary.

param traits: str = 'N/A'

Permanent traits to ascribe to the character.

param verbose: bool = False

chain(prompt: PromptTemplate) → LLMChain

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

generate_dialogue_response(observation: str, now: Optional[datetime] = None) → Tuple[bool, str]

React to a given observation.

generate_reaction(observation: str, now: Optional[datetime] = None) → Tuple[bool, str]

React to a given observation.

get_full_header(force_refresh: bool = False, now: Optional[datetime] = None) → str

Return a full header of the agent’s status, summary, and current time.

get_summary(force_refresh: bool = False, now: Optional[datetime] = None) → str

Return a descriptive summary of the agent.

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 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

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

summarize_related_memories(observation: str) → str

Summarize memories that are most relevant to an observation.

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