ComponentsText embedding modelsMiniMaxMiniMaxMiniMax offers an embeddings service.This example goes over how to use LangChain to interact with MiniMax Inference for text embedding.import osos.environ["MINIMAX_GROUP_ID"] = "MINIMAX_GROUP_ID"os.environ["MINIMAX_API_KEY"] = "MINIMAX_API_KEY"from langchain.embeddings import MiniMaxEmbeddingsembeddings = MiniMaxEmbeddings()query_text = "This is a test query."query_result = embeddings.embed_query(query_text)document_text = "This is a test document."document_result = embeddings.embed_documents([document_text])import numpy as npquery_numpy = np.array(query_result)document_numpy = np.array(document_result[0])similarity = np.dot(query_numpy, document_numpy) / (    np.linalg.norm(query_numpy) * np.linalg.norm(document_numpy))print(f"Cosine similarity between document and query: {similarity}")    Cosine similarity between document and query: 0.1573236279277012PreviousLocalAINextModelScope