Memory Retrieval & Storage

This workflow is designed to enhance personalized interactions by leveraging memory retrieval and storage capabilities. It starts with a '用户输入' (User Input) node where the user's input is captured. The input is then used in the '记忆检索' (Memory Retrieval) node, which queries the Memtensor Memos plugin to fetch relevant memory fragments based on the user's input. The retrieved data is processed through a Python code execution node that extracts memory values and preferences. These extracted details are then passed to an LLM node for generating responses. Finally, the '记忆添加' (Add Memory) node stores both user inputs and AI-generated responses back into the MemOS memory database for future retrievals, supporting chat history management and personalization.