from typing import Any, Dict, List, Union

from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema.messages import BaseMessage, get_buffer_string


class ConversationBufferWindowMemory(BaseChatMemory):
    """Buffer for storing conversation memory inside a limited size window."""

    human_prefix: str = "Human"
    ai_prefix: str = "AI"
    memory_key: str = "history"  #: :meta private:
    k: int = 5
    """Number of messages to store in buffer."""

    @property
    def buffer(self) -> Union[str, List[BaseMessage]]:
        """String buffer of memory."""
        return self.buffer_as_messages if self.return_messages else self.buffer_as_str

    @property
    def buffer_as_str(self) -> str:
        """Exposes the buffer as a string in case return_messages is True."""
        messages = self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else []
        return get_buffer_string(
            messages,
            human_prefix=self.human_prefix,
            ai_prefix=self.ai_prefix,
        )

    @property
    def buffer_as_messages(self) -> List[BaseMessage]:
        """Exposes the buffer as a list of messages in case return_messages is False."""
        return self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else []

    @property
    def memory_variables(self) -> List[str]:
        """Will always return list of memory variables.

        :meta private:
        """
        return [self.memory_key]

    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        """Return history buffer."""
        return {self.memory_key: self.buffer}

langchain.schema.messages.get_buffer_string
[((899, 989), 'langchain.schema.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefix': 'self.ai_prefix'}), '(messages, human_prefix=self.human_prefix, ai_prefix=self.\n    ai_prefix)\n', (916, 989), False, 'from langchain.schema.messages import BaseMessage, get_buffer_string\n')]