ComponentsText embedding modelsSpaCyOn this pageSpaCyspaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.Installation and Setup​#!pip install spacyImport the necessary classesfrom langchain.embeddings.spacy_embeddings import SpacyEmbeddingsExample​Initialize SpacyEmbeddings.This will load the Spacy model into memory.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.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.embeddings = embedder.embed_documents(texts)for i, embedding in enumerate(embeddings):    print(f"Embedding for document {i+1}: {embedding}")Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query.query = "Quick foxes and lazy dogs."query_embedding = embedder.embed_query(query)print(f"Embedding for query: {query_embedding}")PreviousSentence TransformersNextTensorflowHubInstallation and SetupExample