ConversationSummaryBufferMemory

class langchain.memory.summary_buffer.ConversationSummaryBufferMemory

Bases: BaseChatMemory, SummarizerMixin

Buffer with summarizer 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 llm: BaseLanguageModel [Required]

param max_token_limit: int = 2000

param memory_key: str = 'history'

param moving_summary_buffer: str = ''

param output_key: str | None = 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 abuffer() → str | List[BaseMessage]

Async memory buffer.

Return type:

str | List[BaseMessage]

async aclear() → None

Asynchronously clear memory contents.

Return type:

None

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

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

Parameters:

inputs (Dict[str, Any]) –

Return type:

Dict[str, Any]

async apredict_new_summary(messages: List[BaseMessage], existing_summary: str) → str

Parameters:

• messages (List[BaseMessage]) –

• existing_summary (str) –

Return type:

str

async aprune() → None

Asynchronously prune buffer if it exceeds max token limit

Return type:

None

async asave_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None

Asynchronously 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]

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

Parameters:

• messages (List[BaseMessage]) –

• existing_summary (str) –

Return type:

str

prune() → None

Prune buffer if it exceeds max token limit

Return type:

None

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: str | List[BaseMessage]

String buffer of memory.