Earlier quoted context omitted.
I'm aware of more efficient ways to do it! (Hence referencing e.g. doc2query.) But you have to train a model, whereas with an LLM you can get a working version in 5mins of work.
But with even less work you can just pick up a model that was pre-trained using GPL and get great results. I'm able to pull messy results directly from internet sources and re-rank on the fly with a quantized e5 model small enough to fit in a serverless function. You don't need a vector database to do all this stuff, people who are paid off people using vector databases are the ones who are hyping them up the most.
If by "quantized e5 model small enough to fit in a serverless function" you mean e5-small-v2, FYI it actually underperforms just calling OpenAI for embeddings (text-embedding-ada-002) on the HuggingFace MTEB benchmarks. And that definitely doesn't negate using a doc2query-style approach to preprocess the documents before running them through the pretrained embedding model if you're comparing e.g. questions to answers, rather than raw document-to-document similarity. (Of course a custom trained model will be more efficient! In fact, the original doc2query paper in 2019 used a custom trained model for step 1, as did many enhancements on it e.g. doc-t5-query. What's neat is that with the advent of really good pretrained LLMs, you can get results approximating that without training your own models in like ~5mins of work.)