from llama_index.embedding.openai import OpenAIEmbedding from llama_index.core.node_parser import SentenceSplitter nodes = SentenceSplitter()(documents) embeddings = embed_model.get_text_embedding_batch([node.get_content(metadata_mode="embed") for node in nodes]) for (node, embedding) in zip(nodes, embeddings): node.embedding = embedding storage_context.vector_store.add(nodes) # if you are using the default vector store, you might also want to add to the docstore storage_context.docstore.add_documents(nodes) index = VectorStoreIndex(nodes=[], storage_context=storage_context)
