Function init_components

examples/mem_feedback/example_feedback.py:10–150

Initialize MemOS core components. This function is responsible for building and configuring all basic components required for MemOS operation, including: 1. LLM (Large Language Model): Model responsible for natural language understanding and generation (e.g., GPT-4o). 2. Embedder:

()

def init_components():
    """
    Initialize MemOS core components.

    This function is responsible for building and configuring all basic components required for MemOS operation, including:
    1. LLM (Large Language Model): Model responsible for natural language understanding and generation (e.g., GPT-4o).
    2. Embedder: Responsible for converting text into vector representations for semantic search and similarity calculation.
    3. GraphDB (Neo4j): Graph database for persistent storage of memory nodes and their relationships.
    4. MemoryManager: Memory manager responsible for memory CRUD operations.
    5. MemReader: Memory reader for parsing and processing input text.
    6. Reranker: Reranker for refining the sorting of retrieval results.
    7. Searcher: Searcher that integrates retrieval and reranking logic.
    8. FeedbackServer (SimpleMemFeedback): Feedback service core, responsible for processing user feedback and updating memory.

    Returns:
        tuple: (feedback_server, memory_manager, embedder)
    """
    # Lazy import to avoid E402 (module level import not at top of file)
    from memos.configs.embedder import EmbedderConfigFactory
    from memos.configs.graph_db import GraphDBConfigFactory
    from memos.configs.llm import LLMConfigFactory
    from memos.configs.mem_reader import MemReaderConfigFactory
    from memos.configs.reranker import RerankerConfigFactory
    from memos.embedders.factory import EmbedderFactory
    from memos.graph_dbs.factory import GraphStoreFactory
    from memos.llms.factory import LLMFactory
    from memos.mem_feedback.simple_feedback import SimpleMemFeedback
    from memos.mem_reader.factory import MemReaderFactory
    from memos.memories.textual.tree_text_memory.organize.manager import MemoryManager
    from memos.memories.textual.tree_text_memory.retrieve.searcher import Searcher
    from memos.reranker.factory import RerankerFactory

    print("Initializing MemOS Components...")

    # 1. LLM: Configure Large Language Model, using OpenAI compatible interface
    llm_config = LLMConfigFactory.model_validate(
        {
            "backend": "openai",
            "config": {
                "model_name_or_path": os.getenv("MOS_CHAT_MODEL", "gpt-4o"),
                "temperature": 0.8,
                "max_tokens": 1024,
                "top_p": 0.9,
                "top_k": 50,
                "api_key": os.getenv("OPENAI_API_KEY"),
                "api_base": os.getenv("OPENAI_API_BASE"),
            },
        }
    )
    llm = LLMFactory.from_config(llm_config)

    # 2. Embedder: Configure embedding model for generating text vectors
    embedder_config = EmbedderConfigFactory.model_validate(
        {
            "backend": os.getenv("MOS_EMBEDDER_BACKEND", "universal_api"),
            "config": {
                "provider": "openai",
                "api_key": os.getenv("MOS_EMBEDDER_API_KEY", "EMPTY"),