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from abc import ABC
from typing import Any, Dict, Optional, Tuple
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.memory import BaseMemory
from langchain_core.pydantic_v1 import Field
from langchain.memory.chat_message_histories.in_memory import ChatMessageHistory
from langchain.memory.utils import get_prompt_input_key
class BaseChatMemory(BaseMemory, ABC):
"""Abstract base class for chat memory."""
chat_memory: BaseChatMessageHistory = Field(default_factory=ChatMessageHistory)
output_key: Optional[str] = None
input_key: Optional[str] = None
return_messages: bool = False
def _get_input_output(
self, inputs: Dict[str, Any], outputs: Dict[str, str]
) -> Tuple[str, str]:
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
if self.output_key is None:
if len(outputs) != 1:
raise ValueError(f"One output key expected, got {outputs.keys()}")
output_key = list(outputs.keys())[0]
else:
output_key = self.output_key
return inputs[prompt_input_key], outputs[output_key]
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
input_str, output_str = self._get_input_output(inputs, outputs)
self.chat_memory.add_user_message(input_str)
self.chat_memory.add_ai_message(output_str)
def clear(self) -> None:
"""Clear memory contents."""
self.chat_memory.clear()
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