Like the actual embedding vector?
mmm not easily right now.
Here's my hacky solution. You need the node_id, so here I use
index.ref_doc_info
to get a mapping of each injested document and the nodes that came from that document
>>> from llama_index import VectorStoreIndex, Document
>>> index = VectorStoreIndex.from_documents([Document.example()])
>>> index.ref_doc_info
{'e03d1380-fe7d-4828-bb3e-680afeb07bfc': RefDocInfo(node_ids=['a1cbafa9-e2c7-4cea-92b1-3834acb2aa6c'], metadata={'filename': 'README.md', 'category': 'codebase'})}
>>> vector = index.vector_store._data.embedding_dict['a1cbafa9-e2c7-4cea-92b1-3834acb2aa6c']
>>> print(len(vector))
1536
>>>