langchain.memory.summary_buffer — 🦜🔗 LangChain 0.2.0

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):
    """用于存储对话记忆的带有总结器的缓冲区。"""

    max_token_limit: int = 2000
    moving_summary_buffer: str = ""
    memory_key: str = "history"

    @property
    def buffer(self) -> Union[str, List[BaseMessage]]:
        """内存中的字符串缓冲区。"""
        return self.load_memory_variables({})[self.memory_key]

    @property
    def memory_variables(self) -> List[str]:
        """将始终返回内存变量列表。

：元私有：
"""
        return [self.memory_key]

[docs]    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        """返回历史缓冲区。"""
        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:
        """验证提示输入变量是否一致。"""
        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:
        """将此对话的上下文保存到缓冲区中。"""
        super().save_context(inputs, outputs)
        self.prune()

[docs]    def prune(self) -> None:
        """如果超过最大令牌限制，则修剪缓冲区。"""
        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:
        """清除内存内容。"""
        super().clear()
        self.moving_summary_buffer = ""
Source code for langchain.memory.summary_buffer
