Source code for langchain_core.memory

from __future__ import annotations

from abc import ABC, abstractmethod
from typing import Any, Dict, List

from langchain_core.load.serializable import Serializable


[docs]class BaseMemory(Serializable, ABC): """Abstract base class for memory in Chains. Memory refers to state in Chains. Memory can be used to store information about past executions of a Chain and inject that information into the inputs of future executions of the Chain. For example, for conversational Chains Memory can be used to store conversations and automatically add them to future model prompts so that the model has the necessary context to respond coherently to the latest input. Example: .. code-block:: python class SimpleMemory(BaseMemory): memories: Dict[str, Any] = dict() @property def memory_variables(self) -> List[str]: return list(self.memories.keys()) def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: return self.memories def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: pass def clear(self) -> None: pass """ # noqa: E501 class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property @abstractmethod def memory_variables(self) -> List[str]: """The string keys this memory class will add to chain inputs."""
[docs] @abstractmethod def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return key-value pairs given the text input to the chain."""
[docs] @abstractmethod def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save the context of this chain run to memory."""
[docs] @abstractmethod def clear(self) -> None: """Clear memory contents."""