langchain_experimental.generative_agents.memory.GenerativeAgentMemory

class langchain_experimental.generative_agents.memory.GenerativeAgentMemory

Bases: BaseMemory

生成代理的内存。

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

跟踪最近记忆的“重要性”总和。

当达到反思阈值时触发反思。

param current_plan: List[str] = []

代理的当前计划。

param importance_weight: float = 0.15

分配给内存重要性的权重有多大。

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

核心语言模型。

param max_tokens_limit: int = 1200

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

获取相关记忆的检索器。

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

当聚合重要性超过反思阈值时，停止反思。

param relevant_memories_key: str = 'relevant_memories'

param relevant_memories_simple_key: str = 'relevant_memories_simple'

param verbose: bool = False

async aclear() → None

清除内存内容。

Return type

None

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

向代理的记忆中添加观察或记忆。

Parameters

• memory_content (str) –

• now (Optional[datetime]) –

Return type

List[str]

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

将一个观察或记忆添加到agent的记忆中。

Parameters

• memory_content (str) –

• now (Optional[datetime]) –

Return type

List[str]

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

给定文本输入，返回键值对。

Parameters

inputs (Dict[str, Any]) –

Return type

Dict[str, Any]

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

将此链式运行的上下文保存到内存中。

Parameters

• inputs (Dict[str, Any]) –

• outputs (Dict[str, str]) –

Return type

None

chain(prompt: PromptTemplate) → LLMChain

Parameters

prompt (PromptTemplate) –

Return type

LLMChain

clear() → None

清除内存内容。

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]

获取相关的记忆。

Parameters

• observation (str) –

• now (Optional[datetime]) –

Return type

List[Document]

format_memories_detail(relevant_memories: List[Document]) → str

Parameters

relevant_memories (List[Document]) –

Return type

str

format_memories_simple(relevant_memories: List[Document]) → str

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]

获取langchain对象的命名空间。

例如，如果类是`langchain.llms.openai.OpenAI`，那么命名空间是[“langchain”, “llms”, “openai”]

Return type

List[str]

classmethod is_lc_serializable() → bool

这个类是否可序列化？

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]

用于序列化目的的此类的唯一标识符。

唯一标识符是一个描述对象路径的字符串列表。

Return type

List[str]

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

给定文本输入，返回键值对。

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]

反思最近的观察并产生“洞察”。

Parameters

now (Optional[datetime]) –

Return type

List[str]

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

将此模型运行的上下文保存到内存中。

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

需要包含在序列化kwargs中的属性名称列表。

这些属性必须被构造函数接受。

property lc_secrets: Dict[str, str]

构造函数参数名称到秘钥ID的映射。

例如，

{“openai_api_key”: “OPENAI_API_KEY”}

property memory_variables: List[str]

此内存类将动态加载输入密钥。