MemOS-MCP

from memos import log logger = log.get_logger(__name__) def memos_hello_world() -> str: logger.info("memos_hello_world function called.") return "Hello world from memos!" def memos_chend_hello_world() -> str: logger.info("memos_chend_hello_world function called.") return "Hello world from memos-chend!" def memos_wanghy_hello_world() -> str: logger.info("memos_wanghy_hello_world function called.") return "Hello world from memos-wanghy!" def memos_niusm_hello_world() -> str: logger.info("memos_niusm_hello_world function called.") return "Hello world from memos-niusm!" def memos_huojh_hello_world(arr: list) -> list: logger.info("memos_huojh_hello_world function called.") if len(arr) <= 1: return arr else: pivot = arr[0] left = [x for x in arr[1:] if x < pivot] right = [x for x in arr[1:] if x >= pivot] return [*memos_huojh_hello_world(left), pivot, *memos_huojh_hello_world(right)] def memos_dany_hello_world(para_1: int, para_2: str) -> str: logger.info(f"logger.info: para_1 is {para_1}") logger.debug(f"logger.debug: para_2 is {para_2}") return f"return_value_{para_1}" def memos_wangyzh_hello_world() -> str: logger.info("memos_wangyzh_hello_world function called.") return "Hello world from memos-wangyzh!" def memos_zhaojihao_hello_world() -> str: logger.info("memos_zhaojihao_hello_world function called.") return "Hello world from memos-zhaojihao!" def memos_yuqingchen_hello_world() -> str: logger.info("memos_yuqingchen_hello_world function called.") return "Hello world from memos-yuqingchen!" def memos_chentang_hello_world(user_id: str = "locomo_exp_user_1", version: str = "default"): import os from memos.configs.memory import MemoryConfigFactory from memos.memories.factory import MemoryFactory config = MemoryConfigFactory( backend="general_text", config={ "extractor_llm": { "backend": "openai", "config": { "model_name_or_path": os.getenv("MODEL"), "temperature": 0, "max_tokens": 8192, "api_key": os.getenv("OPENAI_API_KEY"), "api_base": os.getenv("OPENAI_BASE_URL"), }, }, "vector_db": { "backend": "qdrant", "config": { "path": f"outputs/locomo/memos-{version}/storages/{user_id}/qdrant", "collection_name": "test_textual_memory", "distance_metric": "cosine", "vector_dimension": 768, # nomic-embed-text model's embedding dimension is 768 }, }, "embedder": { "backend": "ollama", "config": { "model_name_or_path": os.getenv("EMBEDDING_MODEL"), }, }, }, ) memory = MemoryFactory.from_config(config) return memory