Hey apologies, I actually told you the direct way to interact with vector store and retrieve nodes.
But i found something and that is in the same direction that you were looking for.
once you create your retriever, you can create a
QueryBundle
obj which consist embedding field also for your query.
Check the screenshot.
from llama_index.core.schema import QueryBundle
# create retriever
retriever = index.as_retriever()
# create QueryBundle for your query and add embeddings
query = QueryBundle(query_str="This is query", embedding = [ADD Embeddings here])
# pass this object in your retriever to get nodes
nodes = retriever.retrieve(query)