langchain_experimental.generative_agents.memory.GenerativeAgentMemory

class langchain_experimental.generative_agents.memory.GenerativeAgentMemory

Bases: BaseMemory

Memory for the generative agent.

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 add_memory_key: str = 'add_memory'

param aggregate_importance: float = 0.0

Track the sum of the ‘importance’ of recent memories.

Triggers reflection when it reaches reflection_threshold.

param current_plan: List[str] = []

The current plan of the agent.

param importance_weight: float = 0.15

How much weight to assign the memory importance.

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

The core language model.

param max_tokens_limit: int = 1200

param memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever [Required]

The retriever to fetch related memories.

param most_recent_memories_key: str = 'most_recent_memories'

param most_recent_memories_token_key: str = 'recent_memories_token'

param now_key: str = 'now'

param queries_key: str = 'queries'

param reflecting: bool = False

param reflection_threshold: Optional[float] = None

When aggregate_importance exceeds reflection_threshold, stop to reflect.

param relevant_memories_key: str = 'relevant_memories'

param relevant_memories_simple_key: str = 'relevant_memories_simple'

param verbose: bool = False

add_memories(memory_content: str, now: Optional[datetime] = None) → List[str]

Add an observations or memories to the agent’s memory.

add_memory(memory_content: str, now: Optional[datetime] = None) → List[str]

Add an observation or memory to the agent’s memory.

chain(prompt: PromptTemplate) → LLMChain

clear() → None

Clear memory contents.

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.

fetch_memories(observation: str, now: Optional[datetime] = None) → List[Document]

Fetch related memories.

format_memories_detail(relevant_memories: List[Document]) → str

format_memories_simple(relevant_memories: List[Document]) → str

classmethod from_orm(obj: Any) → Model

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”]

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.

load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str]

Return key-value pairs given the text input to the chain.

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

pause_to_reflect(now: Optional[datetime] = None) → List[str]

Reflect on recent observations and generate ‘insights’.

save_context(inputs: Dict[str, Any], outputs: Dict[str, Any]) → None

Save the context of this model run to memory.

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

to_json() → Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() → SerializedNotImplemented

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

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”}

property memory_variables: List[str]

Input keys this memory class will load dynamically.