predict_new_summary(messages: List[BaseMessage], existing_summary: str) → str¶
prune() → None[source]¶
Prune buffer if it exceeds max token limit
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save context from this conversation to buffer.
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
property buffer: List[langchain.schema.messages.BaseMessage]¶
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 ConversationSummaryBufferMemory¶
Set env var OPENAI_API_KEY or load from a .env file:
Conversation Summary Buffer