ConversationBufferMemory#
- class langchain.memory.buffer.ConversationBufferMemory[source]#
Bases:
BaseChatMemoryBuffer for storing conversation memory.
- param ai_prefix: str = 'AI'#
- param chat_memory: BaseChatMessageHistory [Optional]#
- param human_prefix: str = 'Human'#
- param input_key: str | None = None#
- param output_key: str | None = None#
- param return_messages: bool = False#
- async abuffer_as_messages() List[BaseMessage][source]#
Exposes the buffer as a list of messages in case return_messages is False.
- Return type:
List[BaseMessage]
- async abuffer_as_str() str[source]#
Exposes the buffer as a string in case return_messages is True.
- Return type:
str
- async aclear() None#
Clear memory contents.
- Return type:
None
- async aload_memory_variables(inputs: Dict[str, Any]) Dict[str, Any][source]#
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#
Clear memory contents.
- Return type:
None
- load_memory_variables(inputs: Dict[str, Any]) Dict[str, Any][source]#
Return history buffer.
- Parameters:
inputs (Dict[str, Any])
- Return type:
Dict[str, Any]
- save_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
- property buffer: Any#
String buffer of memory.
- property buffer_as_messages: List[BaseMessage]#
Exposes the buffer as a list of messages in case return_messages is False.
- property buffer_as_str: str#
Exposes the buffer as a string in case return_messages is True.
Examples using ConversationBufferMemory