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Cohere Launches Embed 4

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Re: Cohere Launches Embed 4

#21

Post by wrs »

mahjongmen wrote: post #20
simonw wrote: post #17

Earlier quoted context omitted.

I don't doubt the new Cohere model is better - but one of the features I value most from an embedding model is having an escape hatch, so I can continue to calculate vectors using that same model far into the future if something happens to the hosting provider. This matters for embedding models because I'm presumably building up a database of many millions of vectors for later similarity comparisons - so I need to kn…

I'll start off with, I'm not one of our founders and REALLY wouldn't want to be publicly held accountable for policies or commitments until I've been able to get internal alignment on things I say. That being said, since I do manage our Search and Retrieval offering, if we were to deprecate any of our embedding models (which is generally the risk of closed-source models), I will make sure that there is an "escape hat…

To someone building a long term dataset, I’m not sure what assurances would help. Certainly a personal assurance doesn’t (though you’re kind to offer), and even a corporate statement doesn’t (new owners or C-suite could walk that back anytime). It might take a formal third-party “model escrow” arrangement to be really convincing.

Re: Cohere Launches Embed 4

#22

Post by Cheer2171 »

mahjongmen wrote: post #20
simonw wrote: post #17

Earlier quoted context omitted.

I don't doubt the new Cohere model is better - but one of the features I value most from an embedding model is having an escape hatch, so I can continue to calculate vectors using that same model far into the future if something happens to the hosting provider. This matters for embedding models because I'm presumably building up a database of many millions of vectors for later similarity comparisons - so I need to kn…

I'll start off with, I'm not one of our founders and REALLY wouldn't want to be publicly held accountable for policies or commitments until I've been able to get internal alignment on things I say. That being said, since I do manage our Search and Retrieval offering, if we were to deprecate any of our embedding models (which is generally the risk of closed-source models), I will make sure that there is an "escape hat…

> I'm not one of our founders and REALLY wouldn't want to be publicly held accountable for policies or commitments

I don't mean to phrase this in a hostile way, but then what is even the point of posting? Your word means nothing. You are not in a position to promise anything. You could wake up one morning and find yourself laid off with all your accounts terminated.

And the fact that a Cohere employee is playing PR trying to deflect this issue gives me less faith, not more.

Re: Cohere Launches Embed 4

#24

Post by xena »

mahjongmen wrote: post #20
simonw wrote: post #17

Earlier quoted context omitted.

I don't doubt the new Cohere model is better - but one of the features I value most from an embedding model is having an escape hatch, so I can continue to calculate vectors using that same model far into the future if something happens to the hosting provider. This matters for embedding models because I'm presumably building up a database of many millions of vectors for later similarity comparisons - so I need to kn…

I'll start off with, I'm not one of our founders and REALLY wouldn't want to be publicly held accountable for policies or commitments until I've been able to get internal alignment on things I say. That being said, since I do manage our Search and Retrieval offering, if we were to deprecate any of our embedding models (which is generally the risk of closed-source models), I will make sure that there is an "escape hat…

I can claim that my car is able to fly. That does not mean pressing the gas pedal makes it generate lift.

Re: Cohere Launches Embed 4

#25

Post by serjester »

mahjongmen wrote: post #18
moojacob wrote: post #4

Seems to under-perform voyage-3-large on the same benchmark. At the same time, I'm unsure how useful benchmarks are for embeddings.

Which benchmark are you referring to? Voyage-3-large is a text-only and much larger model than Embed-v4. If you want to unlock multimodality with Voyage-3-large, you'd have to either OCR (really bad results usually) or use a VLM to parse your data into textual descriptions (this works alright, but the cost of using a VLM will jack-up your data-pre-processing costs).

I think anyone that cares enough about embedding performance to use niche models is probably parsing their PDF's into some sort of textual format. Otherwise you need orient your all your pipelines to handle images which adds significant complexity (hybrid search, reranking, LLM calls, etc - all way harder with images).

Not to mention an image is optimistically 50 KB vs the same page represented as markdown is maybe 2–5 KB. When you're talking about pulling in potentially hundreds of pages, that's a 10–20x increase in storage, memory usage, and network overhead.

I do wish they had a more head-to-head comparison with voyage. I think they're the de facto king of proprietary embeddings and with Mongo having bought them, I'd love to migrate away once someone can match their performance.

Re: Cohere Launches Embed 4

#26

Post by handfuloflight »

xena wrote: post #24
mahjongmen wrote: post #20

Earlier quoted context omitted.

I'll start off with, I'm not one of our founders and REALLY wouldn't want to be publicly held accountable for policies or commitments until I've been able to get internal alignment on things I say. That being said, since I do manage our Search and Retrieval offering, if we were to deprecate any of our embedding models (which is generally the risk of closed-source models), I will make sure that there is an "escape hat…

I can claim that my car is able to fly. That does not mean pressing the gas pedal makes it generate lift.

What a disingenuous comparison. The contention here is organizational politics, not physics.

Re: Cohere Launches Embed 4

#27

Post by jeffchuber »

neom wrote: post #9

Curious for those in the industry, is there room for Cohere? Apparently they are doing very well in the enterprise, however recently I found myself wondering what their long term value prop is.

enterprise GTM has its own set of challenges and needs and warrants someone really focused on it

Re: Cohere Launches Embed 4

#28

Post by podgietaru »

I built a little RSS Reader / Aggregator that uses Cohere in order to do some arbitrary classification into different topics. I found it incredibly cheap to work with, and pretty good overall at classifying even with very limited inputs.

I also built this into a version of an OpenSource read it later app.

You can check it out here: https://github.com/aws-samples/rss-aggregator-using-cohere-e...

Re: Cohere Launches Embed 4

#29

Post by SparkyMcUnicorn »

CharlieDigital wrote: post #13
SparkyMcUnicorn wrote: post #8

Earlier quoted context omitted.

I had the same thought, although voyage is 32k vs 128k for cohere 4. Anecdotal evidence points to benchmarks correlating with result quality for data I've dealt with. I haven't spent a lot of time comparing results between models, because we were happy with the results after trying a few and tuning some settings. Unless my dataset lines up really well with a benchmark's dataset, creating my own benchmark is probably…

Are people using 32k embeddings and no longer chunking? It feels like embedding content that large -- especially in dense texts -- will lead to loss of fidelity/signal in the output vector.

My understanding is that long context models can create embeddings that are much better at capturing the overall meaning, and are less effective (without chunking) for documents that consist of short standalone sentences.

For example, "The configuration mentioned above is critical" now "knows" what configuration is being referenced, along with which project and anything else talked about in the document.

Re: Cohere Launches Embed 4

#30

Post by serjester »

throwup238 wrote: post #11
simonw wrote: post #5

I have huge respect for Cohere and this embedding model looks like it could be best-in-class, but I find it hard to commit to a proprietary embedding model that's only available via an API when there are such good open weight models available. I really like the approach Nomic take: their most recent models are available via their API or as open weights for non-commercial use only (unless you buy a license). They late…

In my experience, a non-finetunable closed source API is a complete nonstarter for a large fraction of possible use cases, especially the higher value ones like law and engineering. Most of these embedding models are trained too much on colloquial use of language on the internet that has little overlap with how terms of art are used, and without the ability to fine tune it to a specific use case, they're only really…

Have you looked at fine tuning linear adaptors to sit on top of the embedding models? This works with any model (proprietary or open) and I think in practice this is significantly easier to implement anyways.
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