Comment by simonw

If your LLM is good enough you'll likely get better results from tool calling with grep or a FTS engine - the better models can even adapt their search patterns to search for things like "dog OR canine" where previously vector similarity may have been a bigger win.

Getting embeddings working takes a bunch of work: you need to decide on a chunking strategy, then run the embeddings, then decide how best to store them for fast retrieval. You often end up having to keep your embedding store in memory which can add up for larger volumes of data.

I did a whole lot of work with embeddings last year but I've mostly lost interest now that tool-based-search has become so powerful.

Hooking up tool-based-search that itself uses embeddings is worth exploring, but you may find that the results you get from ripgrep are good enough that it's not worth the considerable extra effort.