you left out the part about getting embeddings for your documents. The way I did it was break documents into paragraphs and get embeddings for that. Then format my paragaph data and embeddings into an object I can send into Pinecode
{ vectors: [{id: ‘item_0’, metadata: {text: The text I want returned}, values: [TheEmbeddings]} ]}
and then yeah, query this with vectorized user prompt.