`langchain_community.memory.kg`.ConversationKGMemory¶

class langchain_community.memory.kg.ConversationKGMemory[source]¶

Bases: BaseChatMemory

知识图谈话记忆。

与外部知识图集成，用于存储和检索有关对话中知识三元组的信息。

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 ai_prefix: str = 'AI'¶

param chat_memory: BaseChatMessageHistory [Optional]¶

param entity_extraction_prompt: langchain_core.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:')¶

param human_prefix: str = 'Human'¶

param input_key: Optional[str] = None¶

param k: int = 2¶

param kg: langchain_community.graphs.networkx_graph.NetworkxEntityGraph [Optional]¶

param knowledge_extraction_prompt: langchain_core.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrating them with your knowledge stored within your weights as well as that stored in a knowledge graph. Extract all of the knowledge triples from the last line of conversation. A knowledge triple is a clause that contains a subject, a predicate, and an object. The subject is the entity being described, the predicate is the property of the subject that is being described, and the object is the value of the property.\n\nEXAMPLE\nConversation history:\nPerson #1: Did you hear aliens landed in Area 51?\nAI: No, I didn't hear that. What do you know about Area 51?\nPerson #1: It's a secret military base in Nevada.\nAI: What do you know about Nevada?\nLast line of conversation:\nPerson #1: It's a state in the US. It's also the number 1 producer of gold in the US.\n\nOutput: (Nevada, is a, state)<|>(Nevada, is in, US)<|>(Nevada, is the number 1 producer of, gold)\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: Hello.\nAI: Hi! How are you?\nPerson #1: I'm good. How are you?\nAI: I'm good too.\nLast line of conversation:\nPerson #1: I'm going to the store.\n\nOutput: NONE\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: What do you know about Descartes?\nAI: Descartes was a French philosopher, mathematician, and scientist who lived in the 17th century.\nPerson #1: The Descartes I'm referring to is a standup comedian and interior designer from Montreal.\nAI: Oh yes, He is a comedian and an interior designer. He has been in the industry for 30 years. His favorite food is baked bean pie.\nLast line of conversation:\nPerson #1: Oh huh. I know Descartes likes to drive antique scooters and play the mandolin.\nOutput: (Descartes, likes to drive, antique scooters)<|>(Descartes, plays, mandolin)\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:")¶

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

param output_key: Optional[str] = None¶

param return_messages: bool = False¶

param summary_message_cls: Type[langchain_core.messages.base.BaseMessage] = <class 'langchain_core.messages.system.SystemMessage'>¶: 在上下文中包括的先前话语数量。

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[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

classmethod from_orm(obj: Any) → Model¶

Parameters: obj (Any) –
Return type: Model

get_current_entities(input_string: str) → List[str][source]¶

Parameters: input_string (str) –
Return type: List[str]

get_knowledge_triplets(input_string: str) → List[KnowledgeTriple][source]¶

Parameters: input_string (str) –
Return type: List[KnowledgeTriple]

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][source]¶

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]) → None[source]¶

将此对话中的上下文保存到缓冲区中。

Parameters

inputs (Dict[str, Any]) –
outputs (Dict[str, str]) –

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]¶

将始终返回内存变量列表。

元数据私有

Examples using ConversationKGMemory¶

kg.md

langchain_community.memory.kg.ConversationKGMemory¶

Examples using ConversationKGMemory¶

`langchain_community.memory.kg`.ConversationKGMemory¶