Source code for camel.memories.agent_memories

# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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#     http://www.apache.org/licenses/LICENSE-2.0
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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========

import warnings
from typing import List, Optional

from camel.memories.base import AgentMemory, BaseContextCreator
from camel.memories.blocks import ChatHistoryBlock, VectorDBBlock
from camel.memories.records import ContextRecord, MemoryRecord
from camel.storages import BaseKeyValueStorage, BaseVectorStorage
from camel.types import OpenAIBackendRole


[docs] class ChatHistoryMemory(AgentMemory): r"""An agent memory wrapper of :obj:`ChatHistoryBlock`. Args: context_creator (BaseContextCreator): A model context creator. storage (BaseKeyValueStorage, optional): A storage backend for storing chat history. If `None`, an :obj:`InMemoryKeyValueStorage` will be used. (default: :obj:`None`) window_size (int, optional): The number of recent chat messages to retrieve. If not provided, the entire chat history will be retrieved. (default: :obj:`None`) """ def __init__( self, context_creator: BaseContextCreator, storage: Optional[BaseKeyValueStorage] = None, window_size: Optional[int] = None, ) -> None: if window_size is not None and not isinstance(window_size, int): raise TypeError("`window_size` must be an integer or None.") if window_size is not None and window_size < 0: raise ValueError("`window_size` must be non-negative.") self._context_creator = context_creator self._window_size = window_size self._chat_history_block = ChatHistoryBlock(storage=storage)
[docs] def retrieve(self) -> List[ContextRecord]: records = self._chat_history_block.retrieve(self._window_size) if self._window_size is not None and len(records) == self._window_size: warnings.warn( f"Chat history window size limit ({self._window_size}) " f"reached. Some earlier messages will not be included in " f"the context. Consider increasing window_size if you need " f"a longer context.", UserWarning, stacklevel=2, ) return records
[docs] def write_records(self, records: List[MemoryRecord]) -> None: self._chat_history_block.write_records(records)
[docs] def get_context_creator(self) -> BaseContextCreator: return self._context_creator
[docs] def clear(self) -> None: self._chat_history_block.clear()
[docs] class VectorDBMemory(AgentMemory): r"""An agent memory wrapper of :obj:`VectorDBBlock`. This memory queries messages stored in the vector database. Notice that the most recent messages will not be added to the context. Args: context_creator (BaseContextCreator): A model context creator. storage (BaseVectorStorage, optional): A vector storage storage. If `None`, an :obj:`QdrantStorage` will be used. (default: :obj:`None`) retrieve_limit (int, optional): The maximum number of messages to be added into the context. (default: :obj:`3`) """ def __init__( self, context_creator: BaseContextCreator, storage: Optional[BaseVectorStorage] = None, retrieve_limit: int = 3, ) -> None: self._context_creator = context_creator self._retrieve_limit = retrieve_limit self._vectordb_block = VectorDBBlock(storage=storage) self._current_topic: str = ""
[docs] def retrieve(self) -> List[ContextRecord]: return self._vectordb_block.retrieve( self._current_topic, limit=self._retrieve_limit, )
[docs] def write_records(self, records: List[MemoryRecord]) -> None: # Assume the last user input is the current topic. for record in records: if record.role_at_backend == OpenAIBackendRole.USER: self._current_topic = record.message.content self._vectordb_block.write_records(records)
[docs] def get_context_creator(self) -> BaseContextCreator: return self._context_creator
[docs] class LongtermAgentMemory(AgentMemory): r"""An implementation of the :obj:`AgentMemory` abstract base class for augmenting ChatHistoryMemory with VectorDBMemory. Args: context_creator (BaseContextCreator): A model context creator. chat_history_block (Optional[ChatHistoryBlock], optional): A chat history block. If `None`, a :obj:`ChatHistoryBlock` will be used. (default: :obj:`None`) vector_db_block (Optional[VectorDBBlock], optional): A vector database block. If `None`, a :obj:`VectorDBBlock` will be used. (default: :obj:`None`) retrieve_limit (int, optional): The maximum number of messages to be added into the context. (default: :obj:`3`) """ def __init__( self, context_creator: BaseContextCreator, chat_history_block: Optional[ChatHistoryBlock] = None, vector_db_block: Optional[VectorDBBlock] = None, retrieve_limit: int = 3, ) -> None: self.chat_history_block = chat_history_block or ChatHistoryBlock() self.vector_db_block = vector_db_block or VectorDBBlock() self.retrieve_limit = retrieve_limit self._context_creator = context_creator self._current_topic: str = ""
[docs] def get_context_creator(self) -> BaseContextCreator: r"""Returns the context creator used by the memory. Returns: BaseContextCreator: The context creator used by the memory. """ return self._context_creator
[docs] def retrieve(self) -> List[ContextRecord]: r"""Retrieves context records from both the chat history and the vector database. Returns: List[ContextRecord]: A list of context records retrieved from both the chat history and the vector database. """ chat_history = self.chat_history_block.retrieve() vector_db_retrieve = self.vector_db_block.retrieve( self._current_topic, self.retrieve_limit ) return chat_history[:1] + vector_db_retrieve + chat_history[1:]
[docs] def write_records(self, records: List[MemoryRecord]) -> None: r"""Converts the provided chat messages into vector representations and writes them to the vector database. Args: records (List[MemoryRecord]): Messages to be added to the vector database. """ self.vector_db_block.write_records(records) self.chat_history_block.write_records(records) for record in records: if record.role_at_backend == OpenAIBackendRole.USER: self._current_topic = record.message.content
[docs] def clear(self) -> None: r"""Removes all records from the memory.""" self.chat_history_block.clear() self.vector_db_block.clear()