ConversationBufferMemory#

class langchain.memory.buffer.ConversationBufferMemory[source]#

Bases: BaseChatMemory

Buffer 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() → Any[source]#

String buffer of memory.

Return type:

Any

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

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