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 modelsSpacy EmbeddingOn this pageSpacy EmbeddingLoading the Spacy embedding class to generate and query embeddingsâ€‹Import the necessary classesâ€‹from langchain.embeddings.spacy_embeddings import SpacyEmbeddingsInitialize SpacyEmbeddings.This will load the Spacy model into memory.â€‹embedder = SpacyEmbeddings()Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews.â€‹texts = [    "The quick brown fox jumps over the lazy dog.",    "Pack my box with five dozen liquor jugs.",    "How vexingly quick daft zebras jump!",    "Bright vixens jump; dozy fowl quack.",]Generate and print embeddings for the texts . The SpacyEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification.â€‹embeddings =