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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.
Google search is so bad compared to peak google.
Maybe the web is really that much worse, and the SEO tactics so hostile to genuine content.
But, honestly...just bring back the old google, where I have all kinds of search modifiers to perform exactly the search I want, that just returns all the matching results that have been indexed. Let me sort out the rest. How do you remove the ability to do "exact text search"? It's the most basic of search functions. Remember having "|" modifiers? AND modifiers?
If google launched that again, even as a separate engine, I think they'd have a good product again. Maybe being good doesn't pay the bills for google, though.
Yandex image search is a lot better now.
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
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?
The problem is Gemini is actually kinda dumb. I've had it's answer be sourced from pages that were a decade old on a topic that changes practically by the month.
Have always joked with colleagues about how hard it is to find content in Google Docs, the office suite built by a search company.
I’m astonished at how bad search in Gmail is. I searched for “iPhone receipt” to find out when I purchased my current iPhone, and it pulled up every single email I’ve ever gotten from Best Buy, since they all have a link in there to buy an iPhone, and they all have a link in there to look up a receipt from them.
I know that those emails have both the words “iPhone” and “receipt” but I feel like The Search Engine Company That Also Does Email should have a smarter search engine in their email.
I’m pleasantly surprised that I can ask Gemini for help with it if I specify “look through my Gmail for this information”, but it takes it a minute or two to find it.
Gemini search isn’t great by default, but Deep Research gives noticeably better results.
> performs best with Mixedbread Search, but it can work with any search backend
Bread-first search, is it?
Looks good as I use something similar with the SearXNG MCP, but a shame this isn't an open weight model. There are some wrappers around SearXNG which seem to reduce the token counts returned thus making it easier for the calling model to understand, but a full dedicated model for search is nice. How does it compare with Perplexity, Gemini with search, and Parallel AI? Those are the cloud providers of search based models that I've seen so far.
hey its a search agent for YOUR own data but can also work over the web. Mixedbread is focusing on providing evidence for agents for your internal data. Toast can interact with any search api. You should be able to provide the SearXNG api to it and it should be good to go. Here the default harness: https://github.com/mixedbread-ai/toast-harness
I really wanted this to be a hardware startup - the Juicero of toast.
Sadly its another software company.
I definately got "Unauthorised bread" [1] vibes from the name when I first saw the thread.
Selling you a subscription for slices of toast in individual cartridges for their toasting hardware?
I'm pretty confident we could steal The Though Emporium's Single use thermite based instant hot dog [1] design and make instant packaged toast, fresh to order
Best thing since sliced bread.
"The perfect slice, every time. No muss no fuss."
Seems to me if it doesn't also apply uniform melted butter it would be a hard sell.
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.
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.
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.
All the stupid made up names are taken, startups have to take regular words and make them meaningless now
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
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.
Long time user of your embedding models. I'm trying to understand how this works and if I can leverage it.
It sounds like this is a new layer on top of your existing storage layer? So to use this, would I need to give you all of my data first? Or is there a version that can be run on prem?
you can plugin your existing stack and use it via an openai compatible client.
I've been using https://www.voyageai.com/ for this in production. Might try this out to see how it compares.
I love to spend a dollar for a 70% correct search result
Some days I have no idea what the fuck I am looking at.
Smelling toast is a sign of a stroke, right?
Bah, i was looking forward to a new toaster!
I was looking forward to a new version of the macOS CD/DVD burning software.
What are these names? before opening I thought it was going to be about bread
Nice, a model for google-fu.
I’m disappointed this is some AI thing and not a breadboard company.
I'm disappointed it doesn't burn CDs.
Damn, now I'm hungry. All this bread talk.