Source code for langchain.memory.summary_buffer

from typing import Any, Dict, List, Union

from langchain_core.messages import BaseMessage, get_buffer_string
from langchain_core.pydantic_v1 import root_validator

from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.summary import SummarizerMixin

[docs]class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin): """Buffer with summarizer for storing conversation memory.""" max_token_limit: int = 2000 moving_summary_buffer: str = "" memory_key: str = "history" @property def buffer(self) -> Union[str, List[BaseMessage]]: """String buffer of memory.""" return self.load_memory_variables({})[self.memory_key] @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return history buffer.""" buffer = self.chat_memory.messages if self.moving_summary_buffer != "": first_messages: List[BaseMessage] = [ self.summary_message_cls(content=self.moving_summary_buffer) ] buffer = first_messages + buffer if self.return_messages: final_buffer: Any = buffer else: final_buffer = get_buffer_string( buffer, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix ) return {self.memory_key: final_buffer}
@root_validator() def validate_prompt_input_variables(cls, values: Dict) -> Dict: """Validate that prompt input variables are consistent.""" prompt_variables = values["prompt"].input_variables expected_keys = {"summary", "new_lines"} if expected_keys != set(prompt_variables): raise ValueError( "Got unexpected prompt input variables. The prompt expects " f"{prompt_variables}, but it should have {expected_keys}." ) return values
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" super().save_context(inputs, outputs) self.prune()
[docs] def prune(self) -> None: """Prune buffer if it exceeds max token limit""" buffer = self.chat_memory.messages curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) if curr_buffer_length > self.max_token_limit: pruned_memory = [] while curr_buffer_length > self.max_token_limit: pruned_memory.append(buffer.pop(0)) curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) self.moving_summary_buffer = self.predict_new_summary( pruned_memory, self.moving_summary_buffer )
[docs] def clear(self) -> None: """Clear memory contents.""" super().clear() self.moving_summary_buffer = ""