langchain.memory.summary.ConversationSummaryMemory

class langchain.memory.summary.ConversationSummaryMemory[source]

Bases: BaseChatMemory, SummarizerMixin

Conversation summarizer to chat memory.

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 buffer: str = ''
param chat_memory: BaseChatMessageHistory [Optional]
param human_prefix: str = 'Human'
param input_key: Optional[str] = None
param llm: BaseLanguageModel [Required]
param output_key: Optional[str] = None
param prompt: BasePromptTemplate = PromptTemplate(input_variables=['new_lines', 'summary'], template='Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.\n\nEXAMPLE\nCurrent summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good.\n\nNew lines of conversation:\nHuman: Why do you think artificial intelligence is a force for good?\nAI: Because artificial intelligence will help humans reach their full potential.\n\nNew summary:\nThe human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.\nEND OF EXAMPLE\n\nCurrent summary:\n{summary}\n\nNew lines of conversation:\n{new_lines}\n\nNew summary:')
param return_messages: bool = False
param summary_message_cls: Type[BaseMessage] = <class 'langchain_core.messages.system.SystemMessage'>
async aclear() → None

Clear memory contents.

Return type

None

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 context from this conversation to buffer.

Parameters

  • inputs (Dict[str, Any]) –

  • outputs (Dict[str, str]) –

Return type

None

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

classmethod from_messages(llm: BaseLanguageModel, chat_memory: BaseChatMessageHistory, *, summarize_step: int = 2, **kwargs: Any) → ConversationSummaryMemory[source]
Parameters
Return type

ConversationSummaryMemory

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, Any][source]

Return history buffer.

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

predict_new_summary(messages: List[BaseMessage], existing_summary: str) → str
Parameters

  • messages (List[BaseMessage]) –

  • existing_summary (str) –

Return type

str

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

Save context from this conversation to buffer.

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

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”}

Examples using ConversationSummaryMemory