[verdverm]

If you consider how a mature search product works, that's probably going to give you an improvement over a single embedding method. You'll find that these systems are really an amalgamation of indexes, queries, and rankings. Often an item will have more data and linkage attached to it than just the embedding of the content. They will also perform multiple different queries at the same time and then combine and rank results. If you want Google search response times, you'll go a step further and perform multiple instances of each query so you get your result faster from the first to return.

That being said, people are finding the basic steps you showed above sufficient. There are parameters you can change in these 3 steps. Have you tried changing any of those?

- how you chunk, chunk size and rules - how you embed, which model and size - how you query, the metric(s) used

2 is probably only important to quality of results in that it determines what is available for you to use in the other steps, notably the embedding comparison metric that really defines relevance