If you want a detailed walkthrough on using embeddings for questions about Mars, I would mimic this tutorial.
Basically, you embed all your facts about Mars. Then the incoming question comes in and you embed this. You correlate your incoming question with the entire set of embedded facts. Then based on the top correlations, you pull all the top facts from the database and form a prompt out if this (truncate to fit the limited size of the prompt window). Then you ask GPT-3 to answer the question based on all the top correlated facts in your prompt.
This is probably the best way to extract specific knowledge.
If you fine-tune, it might not be as specific to your facts as you like because you are trying to overcome the noise from the entire set of GPT-3 coefficients (which was trained on the internet, and may not possess your facts).
When it comes to vector databases, you can probably ditch them if you have less than a million embedded facts, but you (or someone helping you) would have to be proficient at database and some amount of coding to achieve this on your own. So don’t get scared away by Pinecone or Weaviate.