langchain_experimental.generative_agents.memory.GenerativeAgentMemory

class langchain_experimental.generative_agents.memory.GenerativeAgentMemory[source]

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: BaseLanguageModel [Required]

The core language model.

param max_tokens_limit: int = 1200
param memory_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
async aclear() → None

Clear memory contents.

Return type

None

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

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

Parameters

  • memory_content (str) –

  • now (Optional[datetime]) –

Return type

List[str]

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

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

Parameters

  • memory_content (str) –

  • now (Optional[datetime]) –

Return type

List[str]

async aload_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any]

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

Parameters

inputs (Dict[str, Any]) –

Return type

Dict[str, Any]

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

Save the context of this chain run to memory.

Parameters

  • inputs (Dict[str, Any]) –

  • outputs (Dict[str, str]) –

Return type

None

chain(prompt: PromptTemplate) → LLMChain[source]
Parameters

prompt (PromptTemplate) –

Return type

LLMChain

clear() → None[source]

Clear memory contents.

Return type

None

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

Parameters

  • _fields_set (Optional[SetStr]) –

  • values (Any) –

Return type

Model

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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

  • update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model

  • self (Model) –

Returns

new model instance

Return type

Model

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.

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

Return type

DictStrAny

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

Fetch related memories.

Parameters

  • observation (str) –

  • now (Optional[datetime]) –

Return type

List[Document]

format_memories_detail(relevant_memories: List[Document]) → str[source]
Parameters

relevant_memories (List[Document]) –

Return type

str

format_memories_simple(relevant_memories: List[Document]) → str[source]
Parameters

relevant_memories (List[Document]) –

Return type

str

classmethod from_orm(obj: Any) → Model
Parameters

obj (Any) –

Return type

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”]

Return type

List[str]

classmethod is_lc_serializable() → bool

Is this class serializable?

Return type

bool

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().

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

  • encoder (Optional[Callable[[Any], Any]]) –

  • models_as_dict (bool) –

  • dumps_kwargs (Any) –

Return type

unicode

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.

Return type

List[str]

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

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

Parameters

inputs (Dict[str, Any]) –

Return type

Dict[str, str]

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

  • path (Union[str, Path]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) → Model
Parameters

obj (Any) –

Return type

Model

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

  • b (Union[str, bytes]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

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

Reflect on recent observations and generate ‘insights’.

Parameters

now (Optional[datetime]) –

Return type

List[str]

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

Save the context of this model run to memory.

Parameters

  • inputs (Dict[str, Any]) –

  • outputs (Dict[str, Any]) –

Return type

None

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

  • by_alias (bool) –

  • ref_template (unicode) –

Return type

DictStrAny

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

  • by_alias (bool) –

  • ref_template (unicode) –

  • dumps_kwargs (Any) –

Return type

unicode

to_json() → Union[SerializedConstructor, SerializedNotImplemented]
Return type

Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() → SerializedNotImplemented
Return type

SerializedNotImplemented

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) → Model
Parameters

value (Any) –

Return type

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.