memoryscope.core.worker.memory_base_worker
- class memoryscope.core.worker.memory_base_worker.MemoryBaseWorker(embedding_model: str = '', generation_model: str = '', rank_model: str = '', **kwargs)[source]
Bases:
BaseWorker- FILE_PATH: str = '/home/runner/work/MemoryScope/MemoryScope/memoryscope/core/worker/memory_base_worker.py'
- __init__(embedding_model: str = '', generation_model: str = '', rank_model: str = '', **kwargs)[source]
Initializes the MemoryBaseWorker with specified models and configurations.
- Parameters:
embedding_model (str) – Identifier or instance of the embedding model used for transforming text.
generation_model (str) – Identifier or instance of the text generation model.
rank_model (str) – Identifier or instance of the ranking model for sorting the retrieved memories wrt. the semantic similarities.
**kwargs – Additional keyword arguments passed to the parent class initializer.
The constructor also initializes key attributes related to memory store, monitoring, user and target identification, and a prompt handler, setting them up for later use.
- property chat_messages: List[List[Message]]
Property to get the chat messages.
- Returns:
List of chat messages.
- Return type:
List[Message]
- property chat_messages_scatter: List[Message]
Property to get the chat messages.
- Returns:
List of chat messages.
- Return type:
List[Message]
- property chat_kwargs: Dict[str, Any]
Retrieves the chat keyword arguments from the context.
This property getter fetches the chat-related parameters stored in the context, which are used to configure how chat interactions are handled.
- Returns:
A dictionary containing the chat keyword arguments.
- Return type:
Dict[str, str]
- property user_name: str
- property target_name: str
- property workflow_name: str
- property language: LanguageEnum
- property embedding_model: BaseModel
Property to get the embedding model. If the model is currently stored as a string, it will be replaced with the actual model instance from the global context’s model dictionary.
- Returns:
The embedding model used for converting text into vector representations.
- Return type:
- property generation_model: BaseModel
Property to access the generation model. If the model is stored as a string, it retrieves the actual model instance from the global context’s model dictionary.
- Returns:
The model used for text generation.
- Return type:
- property rank_model: BaseModel
Property to access the rank model. If the stored rank model is a string, it fetches the actual model instance from the global context’s model dictionary before returning it.
- Returns:
The rank model instance used for ranking tasks.
- Return type:
- property memory_store: BaseMemoryStore
Property to access the memory vector store. If not initialized, it fetches the global memory store.
- Returns:
The memory store instance used for inserting, updating, retrieving and deleting operations.
- Return type:
- property monitor: BaseMonitor
Property to access the monitoring component. If not initialized, it fetches the global monitor.
- Returns:
The monitoring component instance.
- Return type:
- property prompt_handler: PromptHandler
Lazily initializes and returns the PromptHandler instance.
- Returns:
An instance of PromptHandler initialized with specific file path and keyword arguments.
- Return type:
- property memory_manager: MemoryManager
Lazily initializes and returns the MemoryHandler instance.
- Returns:
An instance of MemoryHandler.
- Return type:
MemoryHandler
- get_language_value(languages: dict | List[dict]) Any | List[Any][source]
Retrieves the value(s) corresponding to the current language context.
- Parameters:
languages (dict | list[dict]) – A dictionary or list of dictionaries containing language-keyed values.
- Returns:
The value or list of values matching the current language setting.
- Return type:
Any | list[Any]
- prompt_to_msg(system_prompt: str, few_shot: str, user_query: str, concat_system_prompt: bool = True) List[Message][source]
Converts input strings into a structured list of message objects suitable for AI interactions.
- Parameters:
system_prompt (str) – The system-level instruction or context.
few_shot (str) – An example or demonstration input, often used for illustrating expected behavior.
user_query (str) – The actual user query or prompt to be processed.
concat_system_prompt (bool) – Concat system prompt again or not in the user message. A simple method to improve the effectiveness for some LLMs. Defaults to True.
- Returns:
A list of Message objects, each representing a part of the conversation setup.
- Return type:
List[Message]
- name: str
- workflow_context: Dict[str, Any]
- memoryscope_context: MemoryscopeContext
- raise_exception: bool
- is_multi_thread: bool
- thread_pool: ThreadPoolExecutor
- enable_parallel: bool
- kwargs: dict
- continue_run: bool
- async_task_list: list
- thread_task_list: list