ConversationTokenBufferMemory

class langchain.memory.token_buffer.ConversationTokenBufferMemory

Bases: BaseChatMemory

Conversation chat memory with token limit.

param ai_prefix: str = 'AI'

param chat_memory: BaseChatMessageHistory [Optional]

param human_prefix: str = 'Human'

param input_key: str | None = None

param llm: BaseLanguageModel [Required]

param max_token_limit: int = 2000

param memory_key: str = 'history'

param output_key: str | None = None

param return_messages: bool = False

async aclear() → None

Clear memory contents.

Return type:

None

async aload_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any]

Async return key-value pairs given the text input to the chain.

Parameters:

inputs (Dict[str, Any]) – The inputs to the chain.

Returns:

A dictionary of key-value pairs.

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]

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. Pruned.

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 True.

property buffer_as_str: str

Exposes the buffer as a string in case return_messages is False.