text: language processing tasks, such as document similarity comparison or text classification.â€‹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.â€‹query = "Quick foxes and lazy dogs."query_embedding = embedder.embed_query(query)print(f"Embedding for query: {query_embedding}")PreviousSentence Transformers EmbeddingsNextTensorflowHubLoading the Spacy embedding class to generate and query embeddingsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.