ConversationSummaryBufferMemory#

class langchain.memory.summary_buffer.ConversationSummaryBufferMemory[source]#

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][source]#

Async memory buffer.

Return type:: str | List[BaseMessage]

async aclear() → None[source]#

Asynchronously clear memory contents.

Return type:: None

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

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[source]#

Asynchronously prune buffer if it exceeds max token limit

Return type:: None

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

Asynchronously save context from this conversation to buffer.

Parameters:

inputs (Dict[str, Any])
outputs (Dict[str, str])

Return type:

None

clear() → None[source]#

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]

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

Parameters:

messages (List[BaseMessage])
existing_summary (str)

Return type:

str

prune() → None[source]#

Prune buffer if it exceeds max token limit

Return type:: None

save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#

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.

Examples using ConversationSummaryBufferMemory#

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