langchain_experimental.generative_agents.memory.GenerativeAgentMemory

class langchain_experimental.generative_agents.memory.GenerativeAgentMemory[source]

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][source]

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][source]

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[source]
Parameters

prompt (PromptTemplate) –

Return type

LLMChain

clear() None[source]

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][source]

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[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]

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][source]

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][source]

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[source]

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。