breadislove

yeah, but the bigger the corpus the more you are relying on the LLM guessing the right keyword, if there is no reranking in place, you will just end up burning through a lot of tokens.

coding agents is a very "niche" use-case where lexical matching works very well. code is a graph, by looking at few lines of code you can guess naming conventions etc. codebases are small compared to knowledge bases.

the bigger your index, the more you will need way better tools such as search.

ceramic is very very bad, the worst search provider of all. it feels like it is doing only lexical matching and the index is very outdated

One super important thing missing: Speculative decoding. Things like Dflash(2), Dspark etc. help to do one forwards pass and get 6-7 tokens out of it. (For completeness, the embeddings from the forward pass are passed into a diffusion model which predicts the next tokens, and the model just verifies it (very cheap operation)). So we can produce way more tokens for roughly a similar amount of compute.

I am curious if there is a way to speed it up. Would be very interesting to know how long a human expert takes to finish the task.

you can look it up in the blog. RAG is not super great because of two reasons, single embedding vector models are not that good and stopped improving and second most models are not good at looking up information. we spend great time on improving the modeling side by inventing on the indexing level [1, 2]. and now we trained our model to be very good at search. it is matching the quality of Opus 5 and GPT 5.6 Sol while being faster. it helps your main agent to do the task at greater quality, while reducing cost per task.

[1]: https://www.mixedbread.com/blog/multimodal-late-interaction-... [2]: https://www.mixedbread.com/blog/wholembed-v3

you can try it here: https://dwarkesh-search-demo.vercel.app/

the thing is most agents waste most of their tokens looking up information which can cause context rot. most small models are not as good as looking up information. this model helps to lookup information for your main agent, which helps you to save tokens and still maintain quality.

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

you can plugin your existing stack and use it via an openai compatible client.

https://www.mixedbread.com/docs/agent/chat-completions

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.

[1]: https://www.mixedbread.com/blog/wholembed-v3

there is full lore around the naming. i can guarantee you that we are pretty dedicated around our research and product.

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.

we have not converged at all, if you look at how different the chinese models in terms of architecture you can guess that the labs are experimenting a lot as well. we are seeing all different types of hybrid architectures, different attention methods and so on. Of course on a high level its still a transformer but if you take a proper look we are seeing more divergence then a convergence.

On what do you guys test the model. Its very dubious that there is no common retrieval benchmark such as browsecomp plus or similar tested. And what metric do you report?

adam, i'd like to get in touch and would love to run the benachmark with mixedbread as a search backend. we are doing this right now with a lot of compliance companies. would be very curious how it improves quality/cost e2e

yes, your are right. what heading would you have taken here?

everything worth writing, you should write yourself

ah whoops, I'll fix it. ty!

The ndcg loss is minimal 90.26 -> 89.65. This means it maintains most of the quality.

to which email did you send it? can u send it to support please?

this is the reason why we report ndcg and not recall. ndcg respects fine grained details so you get the an overview of how much details you are trading off since it would hurt the ranking.

yes exactly.

slop complaining about other slop

very bad take. with most modern multomodal models you get way better performance then going to text first

this might be interesting: https://www.theinformation.com/articles/chatgpt-doctors-star...

> $150M RR on just ads, +3x from August. On <1M users.

source: https://x.com/ArfurRock/status/1999618200024076620

a good system (like openevidence) indexes every paper released and semantic search can incredible helpful since the the search api of all those providers are extremely limited in terms of quality.

now you get why those system are not cheap. keeping indexes fresh, maintaining high quality at large scale and being extremely precise is challenging. by having distributed indexes you are at the mercy of the api providers and i can tell you from previous experience that it won't be 'currently accurate'.

for transparency: i am building a search api, so i am biased. but i also build medical retrieval systems for some time.

you should check mixedbread out. we support indexing multimodal data and making data ready for ai. we are adding video and audio support by the end of the year. might be interesting for the OP as well.

we have couple investigative journalists and lawyers using us for a similar usecase.

or deberta but nevertheless super interesting!

For everyone wondering how good this and other benchmarks are:

- the OmniAI benchmark is bad

- Instead check OmniDocBench[1] out

- Mistral OCR is far far behind most Open Source OCR models and even further behind then Gemini

- End to End OCR is still extremely tricky

- composed pipelines work better (layout detection -> reading order -> OCR every element)

- complex table parsing is still extremely difficult

[1]: https://github.com/opendatalab/OmniDocBench

we have extremely processing heavy jobs where user upload large collection of files (audios, pdfs, videos etc.) and expect to get fast processing. its just that we need to fan out sometimes, since a lot of our users a sensitive to processing times.