Hi Michael (and anyone else who wants just over 240K "real world" ada-002 vectors of dimension 1536), you are welcome to retrieve a tar.gz file which contains: - 47K embeddings of Canberra Times news article text from 1994 - 38K embeddings of the first paragraphs of wikipedia articles about organisations - 156.6K embeddings of the first paragraphs of wikipedia articles about people
https://drive.google.com/file/d/13JP_5u7E8oZO6vRg0ekaTgBDQOaj-W00/view?usp=sharing The file is about 1.7GB and will expand to about 4.4GB. This file will be accessible for at least a week, and I hope you dont hit any google drive download limits trying to retrieve it. The embeddings were generated using my openAI account and you are welcome to use them for any purpose you like. best wishes, Kent Fitch On Wed, Apr 12, 2023 at 4:37 PM Michael Wechner <michael.wech...@wyona.com> wrote: > thank you very much for your feedback! > > In a previous post (April 7) you wrote you could make availlable the 47K > ada-002 vectors, which would be great! > > Would it make sense to setup a public gitub repo, such that others could > use or also contribute vectors? > > Thanks > > Michael Wechner > > > Am 12.04.23 um 04:51 schrieb Kent Fitch: > > I only know some characteristics of the openAI ada-002 vectors, although > they are a very popular as embeddings/text-characterisations as they allow > more accurate/"human meaningful" semantic search results with fewer > dimensions than their predecessors - I've evaluated a few different > embedding models, including some BERT variants, CLIP ViT-L-14 (with 768 > dims, which was quite good), openAI's ada-001 (1024 dims) and babbage-001 > (2048 dims), and ada-002 are qualitatively the best, although that will > certainly change! > > In any case, ada-002 vectors have interesting characteristics that I think > mean you could confidently create synthetic vectors which would be hard to > distinguish from "real" vectors. I found this from looking at 47K ada-002 > vectors generated across a full year (1994) of newspaper articles from the > Canberra Times and 200K wikipedia articles: > - there is no discernible/significant correlation between values in any > pair of dimensions > - all but 5 of the 1536 dimensions have an almost identical distribution > of values shown in the central blob on these graphs (that just show a few > of these 1531 dimensions with clumped values and the 5 "outlier" > dimensions, but all 1531 non-outlier dims are in there, which makes for > some easy quantisation from float to byte if you dont want to go the full > kmeans/clustering/Lloyds-algorithm approach): > > https://docs.google.com/spreadsheets/d/1DyyBCbirETZSUAEGcMK__mfbUNzsU_L48V9E0SyJYGg/edit?usp=sharing > > https://docs.google.com/spreadsheets/d/1czEAlzYdyKa6xraRLesXjNZvEzlj27TcDGiEFS1-MPs/edit?usp=sharing > > https://docs.google.com/spreadsheets/d/1RxTjV7Sj14etCNLk1GB-m44CXJVKdXaFlg2Y6yvj3z4/edit?usp=sharing > - the variance of the value of each dimension is characteristic: > > https://docs.google.com/spreadsheets/d/1w5LnRUXt1cRzI9Qwm07LZ6UfszjMOgPaJot9cOGLHok/edit#gid=472178228 > > This probably represents something significant about how the ada-002 > embeddings are created, but I think it also means creating "realistic" > values is possible. I did not use this information when testing recall & > performance on Lucene's HNSW implementation on 192m documents, as I > slightly dithered the values of a "real" set on 47K docs and stored other > fields in the doc that referenced the "base" document that the dithers were > made from, and used different dithering magnitudes so that I could test > recall with different neighbour sizes ("M"), construction-beamwidth and > search-beamwidths. > > best regards > > Kent Fitch > > > > > On Wed, Apr 12, 2023 at 5:08 AM Michael Wechner <michael.wech...@wyona.com> > wrote: > >> I understand what you mean that it seems to be artificial, but I don't >> understand why this matters to test performance and scalability of the >> indexing? >> >> Let's assume the limit of Lucene would be 4 instead of 1024 and there >> are only open source models generating vectors with 4 dimensions, for >> example >> >> >> 0.02150459587574005,0.11223817616701126,-0.007903356105089188,0.03795722872018814 >> >> >> 0.026009393855929375,0.006306684575974941,0.020492585375905037,-0.029064252972602844 >> >> >> -0.08239810913801193,-0.01947402022778988,0.03827739879488945,-0.020566290244460106 >> >> >> -0.007012288551777601,-0.026665858924388885,0.044495150446891785,-0.038030195981264114 >> >> and now I concatenate them to vectors with 8 dimensions >> >> >> >> 0.02150459587574005,0.11223817616701126,-0.007903356105089188,0.03795722872018814,0.026009393855929375,0.006306684575974941,0.020492585375905037,-0.029064252972602844 >> >> >> -0.08239810913801193,-0.01947402022778988,0.03827739879488945,-0.020566290244460106,-0.007012288551777601,-0.026665858924388885,0.044495150446891785,-0.038030195981264114 >> >> and normalize them to length 1. >> >> Why should this be any different to a model which is acting like a black >> box generating vectors with 8 dimensions? >> >> >> >> >> Am 11.04.23 um 19:05 schrieb Michael Sokolov: >> >> What exactly do you consider real vector data? Vector data which is >> based on texts written by humans? >> > We have plenty of text; the problem is coming up with a realistic >> > vector model that requires as many dimensions as people seem to be >> > demanding. As I said above, after surveying huggingface I couldn't >> > find any text-based model using more than 768 dimensions. So far we >> > have some ideas of generating higher-dimensional data by dithering or >> > concatenating existing data, but it seems artificial. >> > >> > On Tue, Apr 11, 2023 at 9:31 AM Michael Wechner >> > <michael.wech...@wyona.com> wrote: >> >> What exactly do you consider real vector data? Vector data which is >> based on texts written by humans? >> >> >> >> I am asking, because I recently attended the following presentation by >> Anastassia Shaitarova (UZH Institute for Computational Linguistics, >> https://www.cl.uzh.ch/de/people/team/compling/shaitarova.html) >> >> >> >> ---- >> >> >> >> Can we Identify Machine-Generated Text? An Overview of Current >> Approaches >> >> by Anastassia Shaitarova (UZH Institute for Computational Linguistics) >> >> >> >> The detection of machine-generated text has become increasingly >> important due to the prevalence of automated content generation and its >> potential for misuse. In this talk, we will discuss the motivation for >> automatic detection of generated text. We will present the currently >> available methods, including feature-based classification as a “first >> line-of-defense.” We will provide an overview of the detection tools that >> have been made available so far and discuss their limitations. Finally, we >> will reflect on some open problems associated with the automatic >> discrimination of generated texts. >> >> >> >> ---- >> >> >> >> and her conclusion was that it has become basically impossible to >> differentiate between text generated by humans and text generated by for >> example ChatGPT. >> >> >> >> Whereas others have a slightly different opinion, see for example >> >> >> >> https://www.wired.com/story/how-to-spot-generative-ai-text-chatgpt/ >> >> >> >> But I would argue that real world and synthetic have become close >> enough that testing performance and scalability of indexing should be >> possible with synthetic data. >> >> >> >> I completely agree that we have to base our discussions and decisions >> on scientific methods and that we have to make sure that Lucene performs >> and scales well and that we understand the limits and what is going on >> under the hood. >> >> >> >> Thanks >> >> >> >> Michael W >> >> >> >> >> >> >> >> >> >> >> >> Am 11.04.23 um 14:29 schrieb Michael McCandless: >> >> >> >> +1 to test on real vector data -- if you test on synthetic data you >> draw synthetic conclusions. >> >> >> >> Can someone post the theoretical performance (CPU and RAM required) of >> HNSW construction? Do we know/believe our HNSW implementation has achieved >> that theoretical big-O performance? Maybe we have some silly performance >> bug that's causing it not to? >> >> >> >> As I understand it, HNSW makes the tradeoff of costly construction for >> faster searching, which is typically the right tradeoff for search use >> cases. We do this in other parts of the Lucene index too. >> >> >> >> Lucene will do a logarithmic number of merges over time, i.e. each doc >> will be merged O(log(N)) times in its lifetime in the index. We need to >> multiply that by the cost of re-building the whole HNSW graph on each >> merge. BTW, other things in Lucene, like BKD/dimensional points, also >> rebuild the whole data structure on each merge, I think? But, as Rob >> pointed out, stored fields merging do indeed do some sneaky tricks to avoid >> excessive block decompress/recompress on each merge. >> >> >> >>> As I understand it, vetoes must have technical merit. I'm not sure >> that this veto rises to "technical merit" on 2 counts: >> >> Actually I think Robert's veto stands on its technical merit already. >> Robert's take on technical matters very much resonate with me, even if he >> is sometimes prickly in how he expresses them ;) >> >> >> >> His point is that we, as a dev community, are not paying enough >> attention to the indexing performance of our KNN algo (HNSW) and >> implementation, and that it is reckless to increase / remove limits in that >> state. It is indeed a one-way door decision and one must confront such >> decisions with caution, especially for such a widely used base >> infrastructure as Lucene. We don't even advertise today in our javadocs >> that you need XXX heap if you index vectors with dimension Y, fanout X, >> levels Z, etc. >> >> >> >> RAM used during merging is unaffected by dimensionality, but is >> affected by fanout, because the HNSW graph (not the raw vectors) is memory >> resident, I think? Maybe we could move it off-heap and let the OS manage >> the memory (and still document the RAM requirements)? Maybe merge RAM >> costs should be accounted for in IW's RAM buffer accounting? It is not >> today, and there are some other things that use non-trivial RAM, e.g. the >> doc mapping (to compress docid space when deletions are reclaimed). >> >> >> >> When we added KNN vector testing to Lucene's nightly benchmarks, the >> indexing time massively increased -- see annotations DH and DP here: >> https://home.apache.org/~mikemccand/lucenebench/indexing.html. Nightly >> benchmarks now start at 6 PM and don't finish until ~14.5 hours later. Of >> course, that is using a single thread for indexing (on a box that has 128 >> cores!) so we produce a deterministic index every night ... >> >> >> >> Stepping out (meta) a bit ... this discussion is precisely one of the >> awesome benefits of the (informed) veto. It means risky changes to the >> software, as determined by any single informed developer on the project, >> can force a healthy discussion about the problem at hand. Robert is >> legitimately concerned about a real issue and so we should use our creative >> energies to characterize our HNSW implementation's performance, document it >> clearly for users, and uncover ways to improve it. >> >> >> >> Mike McCandless >> >> >> >> http://blog.mikemccandless.com >> >> >> >> >> >> On Mon, Apr 10, 2023 at 6:41 PM Alessandro Benedetti < >> a.benede...@sease.io> wrote: >> >>> I think Gus points are on target. >> >>> >> >>> I recommend we move this forward in this way: >> >>> We stop any discussion and everyone interested proposes an option >> with a motivation, then we aggregate the options and we create a Vote maybe? >> >>> >> >>> I am also on the same page on the fact that a veto should come with a >> clear and reasonable technical merit, which also in my opinion has not come >> yet. >> >>> >> >>> I also apologise if any of my words sounded harsh or personal >> attacks, never meant to do so. >> >>> >> >>> My proposed option: >> >>> >> >>> 1) remove the limit and potentially make it configurable, >> >>> Motivation: >> >>> The system administrator can enforce a limit its users need to >> respect that it's in line with whatever the admin decided to be acceptable >> for them. >> >>> Default can stay the current one. >> >>> >> >>> That's my favourite at the moment, but I agree that potentially in >> the future this may need to change, as we may optimise the data structures >> for certain dimensions. I am a big fan of Yagni (you aren't going to need >> it) so I am ok we'll face a different discussion if that happens in the >> future. >> >>> >> >>> >> >>> >> >>> On Sun, 9 Apr 2023, 18:46 Gus Heck, <gus.h...@gmail.com> wrote: >> >>>> What I see so far: >> >>>> >> >>>> Much positive support for raising the limit >> >>>> Slightly less support for removing it or making it configurable >> >>>> A single veto which argues that a (as yet undefined) performance >> standard must be met before raising the limit >> >>>> Hot tempers (various) making this discussion difficult >> >>>> >> >>>> As I understand it, vetoes must have technical merit. I'm not sure >> that this veto rises to "technical merit" on 2 counts: >> >>>> >> >>>> No standard for the performance is given so it cannot be technically >> met. Without hard criteria it's a moving target. >> >>>> It appears to encode a valuation of the user's time, and that >> valuation is really up to the user. Some users may consider 2hours useless >> and not worth it, and others might happily wait 2 hours. This is not a >> technical decision, it's a business decision regarding the relative value >> of the time invested vs the value of the result. If I can cure cancer by >> indexing for a year, that might be worth it... (hyperbole of course). >> >>>> >> >>>> Things I would consider to have technical merit that I don't hear: >> >>>> >> >>>> Impact on the speed of **other** indexing operations. (devaluation >> of other functionality) >> >>>> Actual scenarios that work when the limit is low and fail when the >> limit is high (new failure on the same data with the limit raised). >> >>>> >> >>>> One thing that might or might not have technical merit >> >>>> >> >>>> If someone feels there is a lack of documentation of the >> costs/performance implications of using large vectors, possibly including >> reproducible benchmarks establishing the scaling behavior (there seems to >> be disagreement on O(n) vs O(n^2)). >> >>>> >> >>>> The users *should* know what they are getting into, but if the cost >> is worth it to them, they should be able to pay it without forking the >> project. If this veto causes a fork that's not good. >> >>>> >> >>>> On Sun, Apr 9, 2023 at 7:55 AM Michael Sokolov <msoko...@gmail.com> >> wrote: >> >>>>> We do have a dataset built from Wikipedia in luceneutil. It comes >> in 100 and 300 dimensional varieties and can easily enough generate large >> numbers of vector documents from the articles data. To go higher we could >> concatenate vectors from that and I believe the performance numbers would >> be plausible. >> >>>>> >> >>>>> On Sun, Apr 9, 2023, 1:32 AM Dawid Weiss <dawid.we...@gmail.com> >> wrote: >> >>>>>> Can we set up a branch in which the limit is bumped to 2048, then >> have >> >>>>>> a realistic, free data set (wikipedia sample or something) that >> has, >> >>>>>> say, 5 million docs and vectors created using public data (glove >> >>>>>> pre-trained embeddings or the like)? We then could run indexing on >> the >> >>>>>> same hardware with 512, 1024 and 2048 and see what the numbers, >> limits >> >>>>>> and behavior actually are. >> >>>>>> >> >>>>>> I can help in writing this but not until after Easter. >> >>>>>> >> >>>>>> >> >>>>>> Dawid >> >>>>>> >> >>>>>> On Sat, Apr 8, 2023 at 11:29 PM Adrien Grand <jpou...@gmail.com> >> wrote: >> >>>>>>> As Dawid pointed out earlier on this thread, this is the rule for >> >>>>>>> Apache projects: a single -1 vote on a code change is a veto and >> >>>>>>> cannot be overridden. Furthermore, Robert is one of the people on >> this >> >>>>>>> project who worked the most on debugging subtle bugs, making >> Lucene >> >>>>>>> more robust and improving our test framework, so I'm listening >> when he >> >>>>>>> voices quality concerns. >> >>>>>>> >> >>>>>>> The argument against removing/raising the limit that resonates >> with me >> >>>>>>> the most is that it is a one-way door. As MikeS highlighted >> earlier on >> >>>>>>> this thread, implementations may want to take advantage of the >> fact >> >>>>>>> that there is a limit at some point too. This is why I don't want >> to >> >>>>>>> remove the limit and would prefer a slight increase, such as 2048 >> as >> >>>>>>> suggested in the original issue, which would enable most of the >> things >> >>>>>>> that users who have been asking about raising the limit would >> like to >> >>>>>>> do. >> >>>>>>> >> >>>>>>> I agree that the merge-time memory usage and slow indexing rate >> are >> >>>>>>> not great. But it's still possible to index multi-million vector >> >>>>>>> datasets with a 4GB heap without hitting OOMEs regardless of the >> >>>>>>> number of dimensions, and the feedback I'm seeing is that many >> users >> >>>>>>> are still interested in indexing multi-million vector datasets >> despite >> >>>>>>> the slow indexing rate. I wish we could do better, and vector >> indexing >> >>>>>>> is certainly more expert than text indexing, but it still is >> usable in >> >>>>>>> my opinion. I understand how giving Lucene more information about >> >>>>>>> vectors prior to indexing (e.g. clustering information as Jim >> pointed >> >>>>>>> out) could help make merging faster and more memory-efficient, >> but I >> >>>>>>> would really like to avoid making it a requirement for indexing >> >>>>>>> vectors as it also makes this feature much harder to use. >> >>>>>>> >> >>>>>>> On Sat, Apr 8, 2023 at 9:28 PM Alessandro Benedetti >> >>>>>>> <a.benede...@sease.io> wrote: >> >>>>>>>> I am very attentive to listen opinions but I am un-convinced >> here and I an not sure that a single person opinion should be allowed to be >> detrimental for such an important project. >> >>>>>>>> >> >>>>>>>> The limit as far as I know is literally just raising an >> exception. >> >>>>>>>> Removing it won't alter in any way the current performance for >> users in low dimensional space. >> >>>>>>>> Removing it will just enable more users to use Lucene. >> >>>>>>>> >> >>>>>>>> If new users in certain situations will be unhappy with the >> performance, they may contribute improvements. >> >>>>>>>> This is how you make progress. >> >>>>>>>> >> >>>>>>>> If it's a reputation thing, trust me that not allowing users to >> play with high dimensional space will equally damage it. >> >>>>>>>> >> >>>>>>>> To me it's really a no brainer. >> >>>>>>>> Removing the limit and enable people to use high dimensional >> vectors will take minutes. >> >>>>>>>> Improving the hnsw implementation can take months. >> >>>>>>>> Pick one to begin with... >> >>>>>>>> >> >>>>>>>> And there's no-one paying me here, no company interest >> whatsoever, actually I pay people to contribute, I am just convinced it's a >> good idea. >> >>>>>>>> >> >>>>>>>> >> >>>>>>>> On Sat, 8 Apr 2023, 18:57 Robert Muir, <rcm...@gmail.com> wrote: >> >>>>>>>>> I disagree with your categorization. I put in plenty of work and >> >>>>>>>>> experienced plenty of pain myself, writing tests and fighting >> these >> >>>>>>>>> issues, after i saw that, two releases in a row, vector >> indexing fell >> >>>>>>>>> over and hit integer overflows etc on small datasets: >> >>>>>>>>> >> >>>>>>>>> https://github.com/apache/lucene/pull/11905 >> >>>>>>>>> >> >>>>>>>>> Attacking me isn't helping the situation. >> >>>>>>>>> >> >>>>>>>>> PS: when i said the "one guy who wrote the code" I didn't mean >> it in >> >>>>>>>>> any kind of demeaning fashion really. I meant to describe the >> current >> >>>>>>>>> state of usability with respect to indexing a few million docs >> with >> >>>>>>>>> high dimensions. You can scroll up the thread and see that at >> least >> >>>>>>>>> one other committer on the project experienced similar pain as >> me. >> >>>>>>>>> Then, think about users who aren't committers trying to use the >> >>>>>>>>> functionality! >> >>>>>>>>> >> >>>>>>>>> On Sat, Apr 8, 2023 at 12:51 PM Michael Sokolov < >> msoko...@gmail.com> wrote: >> >>>>>>>>>> What you said about increasing dimensions requiring a bigger >> ram buffer on merge is wrong. That's the point I was trying to make. Your >> concerns about merge costs are not wrong, but your conclusion that we need >> to limit dimensions is not justified. >> >>>>>>>>>> >> >>>>>>>>>> You complain that hnsw sucks it doesn't scale, but when I show >> it scales linearly with dimension you just ignore that and complain about >> something entirely different. >> >>>>>>>>>> >> >>>>>>>>>> You demand that people run all kinds of tests to prove you >> wrong but when they do, you don't listen and you won't put in the work >> yourself or complain that it's too hard. >> >>>>>>>>>> >> >>>>>>>>>> Then you complain about people not meeting you half way. Wow >> >>>>>>>>>> >> >>>>>>>>>> On Sat, Apr 8, 2023, 12:40 PM Robert Muir <rcm...@gmail.com> >> wrote: >> >>>>>>>>>>> On Sat, Apr 8, 2023 at 8:33 AM Michael Wechner >> >>>>>>>>>>> <michael.wech...@wyona.com> wrote: >> >>>>>>>>>>>> What exactly do you consider reasonable? >> >>>>>>>>>>> Let's begin a real discussion by being HONEST about the >> current >> >>>>>>>>>>> status. Please put politically correct or your own company's >> wishes >> >>>>>>>>>>> aside, we know it's not in a good state. >> >>>>>>>>>>> >> >>>>>>>>>>> Current status is the one guy who wrote the code can set a >> >>>>>>>>>>> multi-gigabyte ram buffer and index a small dataset with 1024 >> >>>>>>>>>>> dimensions in HOURS (i didn't ask what hardware). >> >>>>>>>>>>> >> >>>>>>>>>>> My concerns are everyone else except the one guy, I want it >> to be >> >>>>>>>>>>> usable. Increasing dimensions just means even bigger >> multi-gigabyte >> >>>>>>>>>>> ram buffer and bigger heap to avoid OOM on merge. >> >>>>>>>>>>> It is also a permanent backwards compatibility decision, we >> have to >> >>>>>>>>>>> support it once we do this and we can't just say "oops" and >> flip it >> >>>>>>>>>>> back. >> >>>>>>>>>>> >> >>>>>>>>>>> It is unclear to me, if the multi-gigabyte ram buffer is >> really to >> >>>>>>>>>>> avoid merges because they are so slow and it would be DAYS >> otherwise, >> >>>>>>>>>>> or if its to avoid merges so it doesn't hit OOM. >> >>>>>>>>>>> Also from personal experience, it takes trial and error (means >> >>>>>>>>>>> experiencing OOM on merge!!!) before you get those heap >> values correct >> >>>>>>>>>>> for your dataset. This usually means starting over which is >> >>>>>>>>>>> frustrating and wastes more time. >> >>>>>>>>>>> >> >>>>>>>>>>> Jim mentioned some ideas about the memory usage in >> IndexWriter, seems >> >>>>>>>>>>> to me like its a good idea. maybe the multigigabyte ram >> buffer can be >> >>>>>>>>>>> avoided in this way and performance improved by writing bigger >> >>>>>>>>>>> segments with lucene's defaults. But this doesn't mean we can >> simply >> >>>>>>>>>>> ignore the horrors of what happens on merge. merging needs to >> scale so >> >>>>>>>>>>> that indexing really scales. >> >>>>>>>>>>> >> >>>>>>>>>>> At least it shouldnt spike RAM on trivial data amounts and >> cause OOM, >> >>>>>>>>>>> and definitely it shouldnt burn hours and hours of CPU in >> O(n^2) >> >>>>>>>>>>> fashion when indexing. >> >>>>>>>>>>> >> >>>>>>>>>>> >> --------------------------------------------------------------------- >> >>>>>>>>>>> To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org >> >>>>>>>>>>> For additional commands, e-mail: dev-h...@lucene.apache.org >> >>>>>>>>>>> >> >>>>>>>>> >> --------------------------------------------------------------------- >> >>>>>>>>> To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org >> >>>>>>>>> For additional commands, e-mail: dev-h...@lucene.apache.org >> >>>>>>>>> >> >>>>>>> >> >>>>>>> -- >> >>>>>>> Adrien >> >>>>>>> >> >>>>>>> >> --------------------------------------------------------------------- >> >>>>>>> To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org >> >>>>>>> For additional commands, e-mail: dev-h...@lucene.apache.org >> >>>>>>> >> >>>>>> >> --------------------------------------------------------------------- >> >>>>>> To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org >> >>>>>> For additional commands, e-mail: dev-h...@lucene.apache.org >> >>>>>> >> >>>> >> >>>> -- >> >>>> http://www.needhamsoftware.com (work) >> >>>> http://www.the111shift.com (play) >> >> >> > --------------------------------------------------------------------- >> > To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org >> > For additional commands, e-mail: dev-h...@lucene.apache.org >> > >> >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org >> For additional commands, e-mail: dev-h...@lucene.apache.org >> >> >
