All the embedding-enabled GPT-3 apps I've seen do the following: User query -> Retrieve closest embedding's plaintext counterpart -> feed plaintext as context to GPT-3 prompt.
Your vector DB has well formed prompts - users write random stuff, map it to the closest well formed prompt?
"My daily face cream is BrandX's low-sheen formulation" -> "BrandX Matte Face Moisturizer"
OP's example is a little different, because he's not even using Gpt3 completions, he's just using their embeddings API to vectorize product names, then when he gets a new product name, he maps it into the space to find the nearest product names.
But then you have the issue of GPT3 token limits, so you're limited in how many of these relevant snippets you can embed into a prompt. Wondering if there's a better way to go about this (for your first example, rather than OPs use case).
I'm sure the techniques will evolve over time, but for now, these sorts of patterns (pre-index, then augmenting the prompt at query-time) seem to work best for feeding information/context into the model that it doesn't know about. The other broad family of techniques is around trying to train the model with your custom information ("fine-tuning", etc), but I think most practitioners will agree that's currently less effective for these sorts of use-cases. (Disclaimer: I'm not an expert by any means, but I've played around with both techniques and try to keep up-to-date on what the experts are saying).
Like asking 'what streaming services am I paying for and how much have I spent on them to date?', and some tool going over your bank statements to pick out spotify, netflix etc. I could see being useful.
https://simonwillison.net/2023/Jan/13/semantic-search-answer...