MemOS-MCP

from memos.configs.embedder import EmbedderConfigFactory from memos.embedders.factory import EmbedderFactory # Scenario 1: Using EmbedderFactory config = EmbedderConfigFactory.model_validate( { "backend": "ollama", "config": { "model_name_or_path": "nomic-embed-text:latest", }, } ) embedder = EmbedderFactory.from_config(config) text = "This is a sample text for embedding generation." embedding = embedder.embed([text]) print("Scenario 1 embedding shape:", len(embedding[0])) print("==" * 20) # Scenario 2: Batch embedding generation texts = [ "First sample text for batch embedding.", "Second sample text for batch embedding.", "Third sample text for batch embedding.", ] embeddings = embedder.embed(texts) print("Scenario 2 batch embeddings count:", len(embeddings)) print("Scenario 2 first embedding shape:", len(embeddings[0])) print("==" * 20) # Scenario 3: Using SenTranEmbedder config_hf = EmbedderConfigFactory.model_validate( { "backend": "sentence_transformer", "config": { "model_name_or_path": "nomic-ai/nomic-embed-text-v1.5", }, } ) embedder_hf = EmbedderFactory.from_config(config_hf) text_hf = "This is a sample text for Hugging Face embedding generation." embedding_hf = embedder_hf.embed([text_hf]) print("Scenario 3 HF embedding shape:", len(embedding_hf[0]))