Chat memory

ChatMemory

Bases: Memory

The basic class for chat memory model.

Attributes:

Name	Type	Description
llm	LLM	the LLM instance used by this memory.
input_key	Optional[str]	The input key in the model input parameters is used to find the specific query in a
output_key	Optional[str]	The output key in the model output parameters is used to find the specific result
messages	Optional[List[Message]]	The list of conversation messages to send to the LLM memory.

Source code in agentuniverse/agent/memory/chat_memory.py

Python

class ChatMemory(Memory):
    """The basic class for chat memory model.

    Attributes:
        llm (LLM): the LLM instance used by this memory.
        input_key (Optional[str]): The input key in the model input parameters is used to find the specific query in a
        round of conversations.
        output_key (Optional[str]): The output key in the model output parameters is used to find the specific result
        in a round of conversations.
        messages (Optional[List[Message]]): The list of conversation messages to send to the LLM memory.
    """

    llm: Optional[LLM] = None
    input_key: Optional[str] = None
    output_key: Optional[str] = None
    messages: Optional[List[Message]] = None
    prompt_version: Optional[str] = None

    def as_langchain(self) -> BaseChatMemory:
        """Convert the agentUniverse(aU) chat memory class to the langchain chat memory class."""
        if self.llm is None:
            raise ValueError("Must set `llm` when using langchain memory.")
        if self.type is None or self.type == MemoryTypeEnum.SHORT_TERM:
            return AuConversationTokenBufferMemory(llm=self.llm.as_langchain(), memory_key=self.memory_key,
                                                   input_key=self.input_key, output_key=self.output_key,
                                                   max_token_limit=self.max_tokens, messages=self.messages)
        elif self.type == MemoryTypeEnum.LONG_TERM:
            return AuConversationSummaryBufferMemory(llm=self.llm.as_langchain(), memory_key=self.memory_key,
                                                     input_key=self.input_key, output_key=self.output_key,
                                                     max_token_limit=self.max_tokens, messages=self.messages,
                                                     prompt_version=self.prompt_version)

    def set_by_agent_model(self, **kwargs) -> None:
        """ Assign values of parameters to the ChatMemory model in the agent configuration."""
        super().set_by_agent_model(**kwargs)
        if 'messages' in kwargs and kwargs['messages']:
            self.messages = kwargs['messages']
        if 'llm' in kwargs and kwargs['llm']:
            self.llm = kwargs['llm']
        if 'input_key' in kwargs and kwargs['input_key']:
            self.input_key = kwargs['input_key']
        if 'output_key' in kwargs and kwargs['output_key']:
            self.output_key = kwargs['output_key']

    def initialize_by_component_configer(self, component_configer: MemoryConfiger) -> 'ChatMemory':
        """Initialize the chat memory by the ComponentConfiger object.
        Args:
            component_configer(MemoryConfiger): the ComponentConfiger object
        Returns:
            ChatMemory: the ChatMemory object
        """
        super().initialize_by_component_configer(component_configer)
        if hasattr(component_configer, 'input_key') and component_configer.input_key:
            self.input_key = component_configer.input_key
        if hasattr(component_configer, 'output_key') and component_configer.output_key:
            self.output_key = component_configer.output_key
        if hasattr(component_configer, 'prompt_version') and component_configer.prompt_version:
            self.prompt_version = component_configer.prompt_version
        return self

as_langchain()

Convert the agentUniverse(aU) chat memory class to the langchain chat memory class.

Source code in agentuniverse/agent/memory/chat_memory.py

Python

def as_langchain(self) -> BaseChatMemory:
    """Convert the agentUniverse(aU) chat memory class to the langchain chat memory class."""
    if self.llm is None:
        raise ValueError("Must set `llm` when using langchain memory.")
    if self.type is None or self.type == MemoryTypeEnum.SHORT_TERM:
        return AuConversationTokenBufferMemory(llm=self.llm.as_langchain(), memory_key=self.memory_key,
                                               input_key=self.input_key, output_key=self.output_key,
                                               max_token_limit=self.max_tokens, messages=self.messages)
    elif self.type == MemoryTypeEnum.LONG_TERM:
        return AuConversationSummaryBufferMemory(llm=self.llm.as_langchain(), memory_key=self.memory_key,
                                                 input_key=self.input_key, output_key=self.output_key,
                                                 max_token_limit=self.max_tokens, messages=self.messages,
                                                 prompt_version=self.prompt_version)

initialize_by_component_configer(component_configer)

Initialize the chat memory by the ComponentConfiger object. Args: component_configer(MemoryConfiger): the ComponentConfiger object Returns: ChatMemory: the ChatMemory object

Source code in agentuniverse/agent/memory/chat_memory.py

Python

def initialize_by_component_configer(self, component_configer: MemoryConfiger) -> 'ChatMemory':
    """Initialize the chat memory by the ComponentConfiger object.
    Args:
        component_configer(MemoryConfiger): the ComponentConfiger object
    Returns:
        ChatMemory: the ChatMemory object
    """
    super().initialize_by_component_configer(component_configer)
    if hasattr(component_configer, 'input_key') and component_configer.input_key:
        self.input_key = component_configer.input_key
    if hasattr(component_configer, 'output_key') and component_configer.output_key:
        self.output_key = component_configer.output_key
    if hasattr(component_configer, 'prompt_version') and component_configer.prompt_version:
        self.prompt_version = component_configer.prompt_version
    return self

set_by_agent_model(**kwargs)

Assign values of parameters to the ChatMemory model in the agent configuration.

Source code in agentuniverse/agent/memory/chat_memory.py

Python

def set_by_agent_model(self, **kwargs) -> None:
    """ Assign values of parameters to the ChatMemory model in the agent configuration."""
    super().set_by_agent_model(**kwargs)
    if 'messages' in kwargs and kwargs['messages']:
        self.messages = kwargs['messages']
    if 'llm' in kwargs and kwargs['llm']:
        self.llm = kwargs['llm']
    if 'input_key' in kwargs and kwargs['input_key']:
        self.input_key = kwargs['input_key']
    if 'output_key' in kwargs and kwargs['output_key']:
        self.output_key = kwargs['output_key']