I think we picked the 1024 number as something that seemed so large nobody would ever want to exceed it! Obviously that was naive. Still the limit serves as a cautionary point for users; if your vectors are bigger than this, there is probably a better way to accomplish what you are after (eg better off-line training to reduce dimensionality). Is 1024 the magic number? Maybe not, but before increasing I'd like to see some strong evidence that bigger vectors than that are indeed useful as part of a search application using Lucene.
-Mike On Mon, Feb 14, 2022 at 5:08 PM Julie Tibshirani <juliet...@gmail.com> wrote: > > Sounds good, hope the testing goes well! Memory and CPU (largely from more > expensive vector distance calculations) are indeed the main factors to > consider. > > Julie > > On Mon, Feb 14, 2022 at 1:02 PM Michael Wechner <michael.wech...@wyona.com> > wrote: >> >> Hi Julie >> >> Thanks again for your feedback! >> >> I will do some more tests with "all-mpnet-base-v2" (768) and >> "all-roberta-large-v1" (1024), so 1024 is enough for me for the moment :-) >> >> But yes, I could imagine, that eventually it might make sense to allow more >> dimensions than 1024. >> >> Beside memory and "CPU", are there other limiting factors re more >> dimensions? >> >> Thanks >> >> Michael >> >> Am 14.02.22 um 21:53 schrieb Julie Tibshirani: >> >> Hello Michael, the max number of dimensions is currently hardcoded and can't >> be changed. I could see an argument for increasing the default a bit and >> would be happy to discuss if you'd like to file a JIRA issue. However 12288 >> dimensions still seems high to me, this is much larger than most >> well-established embedding models and could require a lot of memory. >> >> Julie >> >> On Mon, Feb 14, 2022 at 12:08 PM Michael Wechner <michael.wech...@wyona.com> >> wrote: >>> >>> Hi Julie >>> >>> Thanks very much for this link, which is very interesting! >>> >>> Btw, do you have an idea how to increase the default max size of 1024? >>> >>> https://lists.apache.org/thread/hyb6w5c4x5rjt34k3w7zqn3yp5wvf33o >>> >>> Thanks >>> >>> Michael >>> >>> >>> >>> Am 14.02.22 um 17:45 schrieb Julie Tibshirani: >>> >>> Hello Michael, I don't have personal experience with these models, but I >>> found this article insightful: >>> https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9. >>> It evaluates the OpenAI models against a variety of existing models on >>> tasks like sentence similarity and text retrieval. Although the other >>> models are cheaper and have fewer dimensions, the OpenAI ones perform >>> similarly or worse. This got me thinking that they might not be a good >>> cost/ effectiveness trade-off, especially the larger ones with 4096 or >>> 12288 dimensions. >>> >>> Julie >>> >>> On Sun, Feb 13, 2022 at 1:55 AM Michael Wechner <michael.wech...@wyona.com> >>> wrote: >>>> >>>> Re the OpenAI embedding the following recent paper might be of interest >>>> >>>> https://arxiv.org/pdf/2201.10005.pdf >>>> >>>> (Text and Code Embeddings by Contrastive Pre-Training, Jan 24, 2022) >>>> >>>> Thanks >>>> >>>> Michael >>>> >>>> Am 13.02.22 um 00:14 schrieb Michael Wechner: >>>> >>>> Here a concrete example where I combine OpenAI model >>>> "text-similarity-ada-001" with Lucene vector search >>>> >>>> INPUT sentence: "What is your age this year?" >>>> >>>> Result sentences >>>> >>>> 1) How old are you this year? >>>> score '0.98860765' >>>> >>>> 2) What was your age last year? >>>> score '0.97811764' >>>> >>>> 3) What is your age? >>>> score '0.97094905' >>>> >>>> 4) How old are you? >>>> score '0.9600177' >>>> >>>> >>>> Result 1 is great and result 2 looks similar, but is not correct from an >>>> "understanding" point of view and results 3 and 4 are good again. >>>> >>>> I understand "similarity" is not the same as "understanding", but I hope >>>> it makes it clearer what I am looking for :-) >>>> >>>> Thanks >>>> >>>> Michael >>>> >>>> >>>> >>>> Am 12.02.22 um 22:38 schrieb Michael Wechner: >>>> >>>> Hi Alessandro >>>> >>>> I am mainly interested in detecting similarity, for example whether the >>>> following two sentences are similar resp. likely to mean the same thing >>>> >>>> "How old are you?" >>>> "What is your age?" >>>> >>>> and that the following two sentences are not similar, resp. do not mean >>>> the same thing >>>> >>>> "How old are you this year?" >>>> "How old have you been last year?" >>>> >>>> But also performance or how OpenAI embeddings compare for example with >>>> SBERT (https://sbert.net/docs/usage/semantic_textual_similarity.html) >>>> >>>> Thanks >>>> >>>> Michael >>>> >>>> >>>> >>>> Am 12.02.22 um 20:41 schrieb Alessandro Benedetti: >>>> >>>> Hi Michael, experience to what extent? >>>> We have been exploring the area for a while given we contributed the first >>>> neural search milestone to Apache Solr. >>>> What is your curiosity? Performance? Relevance impact? How to integrate it? >>>> Regards >>>> >>>> On Fri, 11 Feb 2022, 22:38 Michael Wechner, <michael.wech...@wyona.com> >>>> wrote: >>>>> >>>>> Hi >>>>> >>>>> Does anyone have experience using OpenAI embeddings in combination with >>>>> Lucene vector search? >>>>> >>>>> https://beta.openai.com/docs/guides/embeddings >>>>> >>>>> for example comparing performance re vector size >>>>> >>>>> https://api.openai.com/v1/engines/text-similarity-ada-001/embeddings >>>>> >>>>> and >>>>> >>>>> https://api.openai.com/v1/engines/text-similarity-davinci-001/embeddings >>>>> >>>>> ? >>>>> >>>>> >>>>> Thanks >>>>> >>>>> Michael >>>> >>>> >>>> >>>> >>> >> --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
