text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAleph AlphaAzureOpenAIBedrock EmbeddingsClarifaiCohereDashScopeDeepInfraElasticsearchEmbaasFake EmbeddingsGoogle Cloud Platform Vertex AI PaLMGPT4AllHugging Face HubInstructEmbeddingsJinaLlama-cppLocalAIMiniMaxModelScopeMosaicML embeddingsNLP CloudOpenAISageMaker Endpoint EmbeddingsSelf Hosted EmbeddingsSentence Transformers EmbeddingsSpacy EmbeddingTensorflowHubAgent toolkitsToolsVector storesGrouped by providerIntegrationsText 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: 0.1573236279277012PreviousLocalAINextModelScopeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.