jn2clark

How does it compare to previous work on learning to learn? I don't see it referenced https://arxiv.org/abs/1606.04474

With binary representations you still get 2^D possible configurations so its entirely possible from a representation perspective. The main issue (I think at least) is around determining the similarity. Hamming distance gives an output space of D possible scores. As mentioned in the article, going to 0/1 with cosine gives better granularity as it now penalizes embeddings if they have differing amounts of positive elements in the embedding (i.e. living on different hyper-spheres). It is probably well suited to retrieval where there is a 1:1 correspondence for query-document but if the degeneracy of queries is large then there could be issues discriminating between similar documents. Regimes of binary and (small) dense embeddings could be quite good. I expect a lot more innovation in this space.

That's a great question. I think regimes like that could offer better trade-offs of memory/latency/retrieval performance, although I don't know what they are right now. It also assumes that going to the larger dimensions can preserve more of the full-precision performance which is still TBD. The other thing is how the binary embeddings play with ANN algorithms like HNSW (i.e. recall). With hamming distance the space of similarity scores is quite limited.

I would love an LLM agent that could generate small api examples (reliably) from a repo like this for the various different models and ways to use them.

What is accuracy in this case? is it meant to be recall or is it some evaluation metric?

We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that comes with that. I would love to chat about 1 and 2 so feel free to email me (email is in my profile).

Take a look here https://github.com/marqo-ai/local-image-search-demo. It is based on https://github.com/marqo-ai/marqo. We do a lot of image search applications. Feel free to reach out if you have other questions (email in profile).

Can anyone comment on an open source multi-modal LLM that can produce structured outputs based on an image? I have not found a good open source one yet (this included), seems to be only closed source that can do this reliably well. Any suggestions are very welcome!

Fair enough, apologies for the confusion!

That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.

Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images in a single retrieval step.

As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.

Thanks for the feedback and questions - really appreciate it.

At the moment you would need to do this yourself. It would be possible to have additional preprocessing to accommodate this though. Feel free to add a feature request here https://github.com/marqo-ai/marqo/issues. The other consideration is that you would want the distribution of the content and queries to match what the selected model was trained on.

Regarding metric and dimension - it is really problem dependent as is throughput. Recall and latency numbers reported in benchmarks are typically on very well curated and structured datasets and average across all queries. Recall is not just a function of the HNSW algorithm. I can tell you though you can do 70M vector indexes with 768 dimensions <100ms including inference on very real world datasets. We will publish some benchmarks shortly as we are doing more evaluations on real world data. I also compiled throughput on open CLIP models here as well https://docs.google.com/spreadsheets/d/1ftHKf4MovnAyKhGyi05e.... If there are particular things you want to see let us know and we can add them!

I think it depends a bit on the definition of search here. It might satisfy a literal definition of search but not search as users would expect - which I think is the important point. IMHO vector similarity and vector search are conflated too much and solving search problems as users expect them requires more than similarity.

Good question! At the moment if you have the abstraction of data -> model -> vector then it is amenable to searching like this. It will depend a bit on the use case though.

In the quest for ultimate speed, I started developing a vector database in assembly using gpt4 as a side project https://github.com/jn2clark/GPT4Memory.

If anyone is looking for a vector search engine, see here https://github.com/marqo-ai/marqo. Has additional functionality to make vector search much easier.

This article also has some other methods for hallucination and reference checking by using cross-encoders https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT.... It is not perfect but can be easily modified to make it more robust.

Not sure which one, but if you are after a vector search engine (not just a database) then I can recommend this https://github.com/marqo-ai/marqo. Includes inference, transformations, schema's, multi-modal search, multi-modal queries, multi-modal representations, text chunking and more.

See here https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT... and https://github.com/marqo-ai/marqo/blob/mainline/examples/Spe.... Multiple examples for answering questions from documents/manuals, text, or transcripts.

Not sure on your interest/use case but something that is designed for "documents in" -> "documents out" is here https://github.com/marqo-ai/marqo. It does retrieval using embeddings and combines all the text splitting and inference operations and can be easily deployed to production (its designed for that, not pip install). Works across images and allows for multi-vector representations.

Plenty of good open-source options https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT... . LLM choice is a bit harder but the composability of it all lets you easily choose alternatives.

I think you can and it has some benefits. One interesting thing that can help is to store representations from transformations over the document and then "fuse" the vectors (i.e. average them) at indexing time. You are effectively able to do run-time augmentation but without any extra inference overhead at query time and without increased memory. An easy way to think of this is for similarity measures that are linear (i.e. dot product) you are now scoring the document over a weighted sum of the transformations of the document. Test-time augmentation is a very well known method in ML generally for improving performance and is applicable here. You can do the same for queries as well - akin to query expansion.

You can get pretty far with this https://github.com/marqo-ai/marqo. Choose your LLM of choice to pair with it. Examples https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT...

search engine. it has all the embedding operations (text and images) and optimized inference. lots of other options like multi-modal queries (including negative queries) and multi-modal representations for documents.

Looks really interesting! Are you looking for more vector search integrations? we have one here https://github.com/marqo-ai/marqo which includes a lot of the transformation logic (including inference). If so, we can do a PR

if you want some more options (chunking, models, +more) check here https://github.com/marqo-ai/marqo and an example for RAG using context aware trimming of text for fitting into context windows https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT...

Thanks for the link. Nice to see Marqo on there (disclaimer I am a co-founder of Marqo). For anyone that is interested it includes a really nice api for handling a lot of the manipulations and operations you want to do (adding, updating, patching documents, filtering, embeddings only a subset of fields, multi-modal querying, multi-modal document representations) which are absent from vector db's. It also takes care of inference https://github.com/marqo-ai/marqo