langchain_community.memory.zep_memory
.ZepMemory¶
- class langchain_community.memory.zep_memory.ZepMemory[source]¶
Bases:
ConversationBufferMemory
将您的链历史持久化到Zep MemoryStore。
Zep返回的消息数量以及Zep服务器何时总结聊天历史是可配置的。有关更多详细信息,请参阅Zep文档。
- 示例:
- memory = ZepMemory(
session_id=session_id, # 用于标识您的用户或用户的会话 url=ZEP_API_URL, # 您的Zep服务器的URL api_key=<your_api_key>, # 可选 memory_key=”history”, # 确保与链的提示模板中使用的键匹配 return_messages=True, # 您的提示模板是否期望一个字符串或一个消息列表?
)
chain = LLMChain(memory=memory,…) # 配置您的链以使用ZepMemory实例
- 注意:
要在聊天历史记录旁边持久化元数据,您需要创建一个自定义Chain类,覆盖`prep_outputs`方法,以在调用`self.memory.save_context`时包含元数据。
Zep是一个用于将LLM应用程序投入生产的开源平台。在几分钟内从在LangChain或LlamaIndex中构建的原型或自定义应用程序转到生产环境,无需重写代码。
有关服务器安装说明和更多信息,请参阅: https://docs.getzep.com/deployment/quickstart/
有关zep-python软件包的更多信息,请参见: https://github.com/getzep/zep-python
初始化ZepMemory。
- 参数:
session_id (str): 标识您的用户或用户的会话 url (str, optional): 您的Zep服务器的URL。默认为”http://localhost:8000”。 api_key (Optional[str], optional): 您的Zep API密钥。默认为None。 output_key (Optional[str], optional): 用于输出消息的键。
默认为None。
- input_key (Optional[str], optional): 用于输入消息的键。
默认为None。
- return_messages (bool, optional): 您的提示模板是否期望一个字符串
还是一个消息列表?默认为False 即返回一个字符串。
- human_prefix (str, optional): 用于人类消息的前缀。
默认为”Human”。
- ai_prefix (str, optional): 用于AI消息的前缀。
默认为”AI”。
- memory_key (str, optional): 用于存储的键。
默认为”history”。 确保与链的提示模板中使用的键匹配。
- param ai_prefix: str = 'AI'¶
- param chat_memory: ZepChatMessageHistory [Required]¶
- param human_prefix: str = 'Human'¶
- param input_key: Optional[str] = None¶
- param output_key: Optional[str] = None¶
- param return_messages: bool = False¶
- async abuffer() Any ¶
内存中的字符串缓冲区。
- Return type
Any
- async abuffer_as_messages() List[BaseMessage] ¶
在return_messages为False的情况下,将缓冲区公开为消息列表。
- Return type
List[BaseMessage]
- async abuffer_as_str() str ¶
如果return_messages为True,则将缓冲区公开为字符串。
- Return type
str
- async aclear() None ¶
清除内存内容。
- Return type
None
- 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 ¶
将此对话的上下文保存到缓冲区中。
- Parameters
inputs (Dict[str, Any]) –
outputs (Dict[str, str]) –
- Return type
None
- 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
- 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, Any] ¶
返回历史缓冲区。
- Parameters
inputs (Dict[str, Any]) –
- Return type
Dict[str, Any]
- 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
- save_context(inputs: Dict[str, Any], outputs: Dict[str, str], metadata: Optional[Dict[str, Any]] = None) None [source]¶
将此对话的上下文保存到缓冲区中。
- 参数:
inputs(Dict[str, Any]):链的输入。 outputs(Dict[str, str]):链的输出。 metadata(Optional[Dict[str, Any],可选):要与上下文一起保存的任何元数据。默认为None
- 返回:
无
- Parameters
inputs (Dict[str, Any]) –
outputs (Dict[str, str]) –
metadata (Optional[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
- to_json_not_implemented() SerializedNotImplemented ¶
- Return type
- 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 buffer: Any¶
内存中的字符串缓冲区。
- property buffer_as_messages: List[langchain_core.messages.base.BaseMessage]¶
在return_messages为False的情况下,将缓冲区公开为消息列表。
- property buffer_as_str: str¶
如果return_messages为True,则将缓冲区公开为字符串。
- 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]¶
将始终返回内存变量列表。
- 元数据 私有