# load the index index = load_index_from_storage(...) # get the nodes and embeddings nodes = index.docstore.docs embeddings = index.vector_store._data.embedding_dict # attach the embeddings nodes_with_embeddings = [] for node_id, node in nodes.items(): node.embedding = embeddings[node_id] nodes_with_embeddings.append(node) # create a new index with the new backend (i.e. qdrant, chroma, weaviate) vector_index = VectorStoreIndex(nodes_with_embeddings, storage_context=storage_context)
