ComponentsText embedding modelsEmbaasOn this pageEmbaasembaas is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a variety of pre-trained models.In this tutorial, we will show you how to use the embaas Embeddings API to generate embeddings for a given text.Prerequisites​Create your free embaas account at https://embaas.io/register and generate an API key.# Set API keyembaas_api_key = "YOUR_API_KEY"# or set environment variableos.environ["EMBAAS_API_KEY"] = "YOUR_API_KEY"from langchain.embeddings import EmbaasEmbeddingsembeddings = EmbaasEmbeddings()# Create embeddings for a single documentdoc_text = "This is a test document."doc_text_embedding = embeddings.embed_query(doc_text)# Print created embeddingprint(doc_text_embedding)# Create embeddings for multiple documentsdoc_texts = ["This is a test document.", "This is another test document."]doc_texts_embeddings = embeddings.embed_documents(doc_texts)# Print created embeddingsfor i, doc_text_embedding in enumerate(doc_texts_embeddings):    print(f"Embedding for document {i + 1}: {doc_text_embedding}")# Using a different model and/or custom instructionembeddings = EmbaasEmbeddings(    model="instructor-large",    instruction="Represent the Wikipedia document for retrieval",)For more detailed information about the embaas Embeddings API, please refer to the official embaas API documentation.PreviousElasticsearchNextERNIE Embedding-V1Prerequisites