I deeply love this idea of specialized LLMs for search. It's also extremely confusing to me how rough Google's entrance here is.
When I, a human, need an answer to anything moderately complex, it's unlikely that I get it on the first (pre-AI) round of google searching. Simple stuff, sure, but more likely I'll need to go 2-5 rounds. Maybe click a few links. Double-check my assumptions.
An LLM that can do that quickly seems like a slam dunk. I wonder what other problems benefit from that 10x-100x increase in context + 2-5 rounds with the LLM.
About 15 years ago I would sometimes spend hours on Google image search discovering childhood toys and filling in vague memories of locations or things. I tried this recently and it’s basically impossible. I actually get to the end of the search results in like 3 minutes and the quality is horrible now.
I think in a lot of ways Google peaked and is now on the decline into a profitable but much less relevant services company.
For example, a couple of days ago I described a problem with my refrigerator's water dispenser to Google Gemini, and it told me exactly how to fix it. I then went looking for a video and fixed the thing in under 15 minutes. The only way that Gemini could have been better is if it linked to a video itself.
Do you mean search into less-well-known topics? Or something else?
I know it can be a deep time sink, but I notice more and more how much deeper my understanding is of a certain problem/best-practice after developing the neuropathways involved in crawling between reddit, stack overflow, etc, to get to the proper solution. I love the instant answer from google ai, but I also notice an itch to purposefully force myself to ignore it when time allows.
I guess someone who has used a search agent (or a dedicated subagent) can speak when I'd reach for a tool like this vs either just 1) a smaller general model or 2) a non-llm approach to the problem? Like it's interesting I'm just curious how a search agent compares to say a model with dedicated rag pipelines is that much different?
the issue with smaller general models (see at the charts) are way behind the frontier models when it comes to search. we've found that there is huge uplift of having a fast dedicated model. from our perspective, having a very good index is the biggest lever and then having a specialised model.
yes for the retrieval benchmarks. For officeqa pro v2 we used Codex (as databricks did) and for Harvey LAB we used the vanilla harvey benchmark. For these benchmarks we added minimal tools to use mixedbread search and toast 1.
Mixedbread Search is a multimodal & multilingual search product, where you can upload any kind of data and make it searchable. Its powered by Wholembed [1] v3, a late interaction retrieval model.
I know everyone loves to hate on google but i find search overviews and asking gemini to search for things way faster than any alternative. I was curious about a development near me and asked literally that and gemini pulled court records in about 20 seconds
Anyway, back to this - it seems to be more like the AI equivalent of algolia than google
If you get big, the naming no longer matters. We made jokes about the Wii until everybody had one. But if you don't get big, and most people have no idea what they're looking at when they see your product for the first time, naming definitely matters.
I deeply love this idea of specialized LLMs for search. It's also extremely confusing to me how rough Google's entrance here is.
When I, a human, need an answer to anything moderately complex, it's unlikely that I get it on the first (pre-AI) round of google searching. Simple stuff, sure, but more likely I'll need to go 2-5 rounds. Maybe click a few links. Double-check my assumptions.
An LLM that can do that quickly seems like a slam dunk. I wonder what other problems benefit from that 10x-100x increase in context + 2-5 rounds with the LLM.
About 15 years ago I would sometimes spend hours on Google image search discovering childhood toys and filling in vague memories of locations or things. I tried this recently and it’s basically impossible. I actually get to the end of the search results in like 3 minutes and the quality is horrible now.
I think in a lot of ways Google peaked and is now on the decline into a profitable but much less relevant services company.
What kind of search do you mean?
For example, a couple of days ago I described a problem with my refrigerator's water dispenser to Google Gemini, and it told me exactly how to fix it. I then went looking for a video and fixed the thing in under 15 minutes. The only way that Gemini could have been better is if it linked to a video itself.
Do you mean search into less-well-known topics? Or something else?
I know it can be a deep time sink, but I notice more and more how much deeper my understanding is of a certain problem/best-practice after developing the neuropathways involved in crawling between reddit, stack overflow, etc, to get to the proper solution. I love the instant answer from google ai, but I also notice an itch to purposefully force myself to ignore it when time allows.
ime google ai is wrong often enough that I'm still doing that to verify its results
At least validation seems faster than without, but you get what you pay for when it comes to llm intelligence
I guess someone who has used a search agent (or a dedicated subagent) can speak when I'd reach for a tool like this vs either just 1) a smaller general model or 2) a non-llm approach to the problem? Like it's interesting I'm just curious how a search agent compares to say a model with dedicated rag pipelines is that much different?
the issue with smaller general models (see at the charts) are way behind the frontier models when it comes to search. we've found that there is huge uplift of having a fast dedicated model. from our perspective, having a very good index is the biggest lever and then having a specialised model.
interesting, thanks for the reply.
Are the benchmarks comparing just the models while keeping the harness the same (the open-source Toast harness)?
yes for the retrieval benchmarks. For officeqa pro v2 we used Codex (as databricks did) and for Harvey LAB we used the vanilla harvey benchmark. For these benchmarks we added minimal tools to use mixedbread search and toast 1.
I’m disappointed this is some AI thing and not a breadboard company.
I'm disappointed it doesn't burn CDs.
> performs best with Mixedbread Search, but it can work with any search backend
Bread-first search, is it?
Article should probably explain what "Mixedbread Search" is.
Mixedbread Search is a multimodal & multilingual search product, where you can upload any kind of data and make it searchable. Its powered by Wholembed [1] v3, a late interaction retrieval model.
[1]: https://www.mixedbread.com/blog/wholembed-v3
Some days I have no idea what the fuck I am looking at.
I know everyone loves to hate on google but i find search overviews and asking gemini to search for things way faster than any alternative. I was curious about a development near me and asked literally that and gemini pulled court records in about 20 seconds
Anyway, back to this - it seems to be more like the AI equivalent of algolia than google
Dunno about this branding/naming scheme - every time it comes up we have to double-check that it isn't some spoof/joke page
there is full lore around the naming. i can guarantee you that we are pretty dedicated around our research and product.
An inside joke that confuses your target audience and makes you sound like a joke, is probably not what you want as your brand.
The brand only needs to last until the Google acquihire.
(FWIW I like the branding. Hugging Face doesn't seem to struggle because of its name either)
If you get big, the naming no longer matters. We made jokes about the Wii until everybody had one. But if you don't get big, and most people have no idea what they're looking at when they see your product for the first time, naming definitely matters.
lore does not make it a good or professional choice
Damn, now I'm hungry. All this bread talk.