If embeddings are roughly the equivalent of a hash at least insofar as they transform a large input into some kind of "content-addressed distillation" (ignoring the major difference that a hash is opaque whereas an embedding has intrinsic meaning), has there been any research done on "cracking" them? That is, starting from an embedding and working backwards to generate a piece of text that is semantically close by?
I could imagine an LLM inference pipeline where the next token ranking includes its similarity to the target embedding, or perhaps instead the change in direction towards/away from the desired embedding that adding it would introduce.
What if you could do that but for whole bodies of text?
I'm imagining being able to do "semantic algebra" with whole paragraphs/articles/books. Instead of just prompting an LLM to "adjust the tone to be more friendly", you could have the core concept of "friendly" (or some more nuanced variant thereof) and "add" it to your existing text, etc.
computerex · 51s ago
This article really rubbed me the wrong way.
> I could tell you exactly how I think we might advance the state of the art in technical writing with embeddings, but where’s the fun in that? You now know why they’re such an interesting and useful new tool in the technical writer toolbox… go connect the rest of the dots yourself!
I read the article because of the title, only to find the above.
tyho · 1h ago
> The 2D map analogy was a nice stepping stone for building intuition but now we need to cast it aside, because embeddings operate in hundreds or thousands of dimensions. It’s impossible for us lowly 3-dimensional creatures to visualize what “distance” looks like in 1000 dimensions. Also, we don’t know what each dimension represents, hence the section heading “Very weird multi-dimensional space”.5 One dimension might represent something close to color. The king - man + woman ≈ queen anecdote suggests that these models contain a dimension with some notion of gender. And so on. Well Dude, we just don’t know.
nit. This suggests that the model contains a direction with some notion of gender, not a dimension. Direction and dimension appear to be inextricably linked by definition, but with some handwavy maths, you find that the number of nearly orthogonal dimensions within n dimensional space is exponential with regards to n. This helps explain why spaces on the order of 1k dimensions can "fit" billions of concepts.
PaulHoule · 1h ago
Note you don't see arXiv papers where somebody feeds in 1000 male gendered words into a word embedding and gets 950 correct female gendered words. Statistically it does better than chance, but word embeddings don't do very well.
there are a number of graphs where they have about N=20 points that seem to fall in "the right place" but there are a lot of dimensions involved and with 50 dimensions to play with you can always find a projection that makes the 20 points fall exactly where you want them fall. If you try experiments with N>100 words you go endlessly in circles and produce the kind of inconclusively negative results that people don't publish.
The BERT-like and other transformer embeddings far outperform word vectors because they can take into account the context of the word. For instance you can't really build a "part of speech" classifier that can tell you "red" is an adjective because it is also a noun, but give it the context and you can.
In the context of full text search, bringing in synonyms is a mixed bag because a word might have 2 or 3 meanings and the the irrelevant synonyms are... irrelevant and will bring in irrelevant documents. Modern embeddings that recognize context not only bring in synonyms but the will suppress usages of the word with different meanings, something the IR community has tried to figure out for about 50 years.
manmal · 1m ago
Don’t the high end embedding services use a transformer with attention to compute embeddings? If so, I thought that would indeed capture the semantic meaning quite well, including the trait-is-described-by-direction-vector.
minimaxir · 1h ago
> The BERT-like and other transformer embeddings far outperform word vectors because they can take into account the context of the word.
In addition to being able to utilize attention mechanisms, modern embedding models use a form of tokenization such as BPE which a) includes punctuation which is incredibly important for extracting semantic meaning and b) includes case, without as much memory requirements as a cased model.
The original BERT used an uncased, SentencePiece tokenizer which is out of date nowadays.
PaulHoule · 57m ago
I was working at a startup that was trying to develop foundation models around at time and BPE was such a huge improvement over everything else we'd tried at that time. We had endless meetings where people proposed that we use various embeddings that would lose 100% of the information for out-of-dictionary words and I'd point out that out-of-dictionary words (particularly from the viewpoint of the pretrained model) frequently meant something critical and if we lost that information up front we couldn't get it back.
Little did I know that people were going to have a lot of tolerance for "short circuiting" of LLMs, that is getting the right answer by the wrong path, so I'd say now that my methodology of "predictive evaluation" that would put an upper bound on what a system could do was pessimistic. Still I don't like giving credit for "right answer by wrong means" since you can't count on it.
philipwhiuk · 54m ago
> In https://nlp.stanford.edu/projects/glove/ there are a number of graphs where they have about N=20 points that seem to fall in "the right place" but there are a lot of dimensions involved and with 50 dimensions to play with you can always find a projection that makes the 20 points fall exactly where you want them fall.
Ramsey theory (or 'the Woolworths store alignment hypothesis')
kaycebasques · 1h ago
Oh yes, this makes a lot of sense, thank you for the "nit" (which doesn't feel like a nit to me, it feels like an important conceptual correction). When I was writing the post I definitely paused at that part, knowing that something was off about describing the model as having a dimension that maps to gender. As you said, since the models are general-purpose and work so well in so many domains, there's no way that there's a 1-to-1 correspondence between concepts and dimensions.
I think your comment is also clicking for me now because I previously did not really understand how cosine similarity worked, but then watched videos like this and understand it better now: https://youtu.be/e9U0QAFbfLI
I will eventually update the post to correct this inaccuracy, thank you for improving my own wetware's conceptual model of embeddings
OJFord · 1h ago
I would think of it as the whole embedding concept again on a finer grained scale: you wouldn't say the model 'has a dimension of whether the input is king', instead the embedding expresses the idea of 'king' with fewer dimensions than would be needed to cover all ideas/words/tokens like that.
So the distinction between a direction and a dimension expressing 'gender' is that maybe gender isn't 'important' (or I guess high-information-density) enough to be an entire dimension, but rather is expressed by a linear combination of two (or more) yet more abstract dimensions.
benatkin · 59m ago
> Machine learning (ML) has the potential to advance the state of the art in technical writing. No, I’m not talking about text generation models like Claude, Gemini, LLaMa, GPT, etc. The ML technology that might end up having the biggest impact on technical writing is embeddings.
This is maybe showing some age as well, or maybe not. It seems that text generation will soon be writing top tier technical docs - the research done on the problem with sycophancy will likely result something significantly better than what LLMs had before the regression to sycophancy. Either way, I take "having the biggest impact on technical writing" to mean in the near term. If having great search and organization tools (ambient findability and such) is going to steal the thunder from LLMs writing really good technical docs, it's going to need to happen fast.
rahimnathwani · 38m ago
Nice article related to the last point (nearly orthogonal vectors):
>nearly orthogonal dimensions within n dimensional space
nit within a nit: I believe you intended to write "nearly orthogonal directions within n dimensional space" which is important as you are distinguishing direction from dimension in your post.
gweinberg · 44m ago
It's not at all a nit. If one of the dimensions did indeed correspond to gender, you might find "king" and "queen" pretty much only differed in one dimension. More generally, if these dimensions individually refer to human-meaningful concepts, you can find out what these concepts are just by looking at words that pretty much differ only along one dimension.
daxfohl · 1h ago
Wait, but if gender was composed of say two dimensions, then there'd be no way to distinguish between "the gender is different" and "the components represented by each of those dimensions are individually different", right?
daxfohl · 45m ago
Oh, so I think what it does is take a nearly infinite-dimensional nonlinear space, and transform it into "the N dimensional linear space that best preserves approximations of linear combinations of elements". That way, any two (or more) terms can combine to make others, so there isn't such a thing as "prime" terms (similar to real dictionaries, every word is defined in terms of other words). Though some, like gender, may have strong enough correlations so as to be approximately prime in a large enough space. Is that about right?
drc500free · 54m ago
Is this because we can essentially treat each dimension like a binary digit, so we get 2^n directions we can encode? Or am I barking up totally the wrong tree?
osigurdson · 1h ago
You can't visualize it but you can certainly compute the euclidean distance. Tools like UMAP can be used to drop the dimensionality as well.
minimaxir · 51m ago
Speaking of UMAP, a new update to the cuML library (https://github.com/rapidsai/cuml) released last month allows UMAP to feasibly be used on big data without shenanigans/spending a lot of money. This opens up quite a few new oppertunities and I'm getting very good results with.
To be clear, when I said "embeddings are underrated" I was only arguing that my fellow technical writers (TWs) were not paying enough attention to a very useful new tool in the TW toolbox. I know that the statement sounds silly to ML practitioners, who very much don't "underrate" embeddings.
I know that the post is light on details regarding how exactly we apply embeddings in TW. I have some projects and other blog posts in the pipeline. Short story long, embeddings are important because they can help us make progress on the 3 intractable challenges of TW: https://technicalwriting.dev/strategy/challenges.html
sgbeal · 41m ago
> I know that the post is light on details regarding how exactly we apply embeddings in TW.
More significantly, after having read the first 6 or 8 paragraphs, i still have no clue what an "embedding" is. From the 3rd paragraph:
> Here’s an overview of how you use embeddings and how they work.
But no mention of what they are (unless perhaps it's buried far deeper in the article).
theletterf · 29m ago
Perhaps you should make the post more appealing to tech writers and less to ML experts. That would help increase the reach for the intended target audience. For example, you can expand on "the ability to discover connections between texts at previously impossible scales". There's an applications section, but it's easy to overlook. Frontload value for tech writers with examples.
rybosome · 1h ago
Thanks for the write-up!
I’m curious how you found the quality of the results? This gets into evals which ML folks love, but even just with “vibes” do the results eyeball as reasonable to you?
bawolff · 23m ago
> I could tell you exactly how I think we might advance the state of the art in technical writing with embeddings, but where’s the fun in that? You now know why they’re such an interesting and useful new tool in the technical writer toolbox… go connect the rest of the dots yourself!
Wow, that's bold. I guess "good" technical writing no longer includes a thesis statement.
Seriously though, why would this be useful for technical writing? Sure you could make some similar pages widget however i dont think i've ever wanted that when reading technical docs, let alone writing them.
simonw · 15m ago
Related documents aside, technical documentation benefits from really great search.
Embeddings are a _very_ useful tool for building better search - they can handle "fuzzy" matches, where a user can say things like "that feature that lets me run a function against every column of data" because they can't remember the name of the feature.
With embeddings you can implement a hybrid approach, where you mix both keyword search (still necessary because embeddings can miss things that use jargon they weren't trained on) and vector similarity search.
I wish I had good examples to point to for this!
ubj · 25m ago
> Because we always get back the same amount of numbers no matter how big or small the input text, we now have a way to mathematically compare any two pieces of arbitrary text to each other.
I think there needs to be some more clarification here. Hash functions also return the same sized output no matter how big or small the input text. However, mathematically comparing two hashes is going to have a much different meaning than mathematically comparing two embeddings.
I'd recommend emphasizing that embeddings are training dependent--the quality of comparison will depend on the quality and type of training used to produce the embedding. There isn't some single "universal embedding" that allows for meaningful comparison of arbitrary text.
adefa · 26m ago
Here’s a CLI I’m experimenting with https://github.com/TrevorS/rhizome that indexes local repos with Tree‑sitter, stores ONNX embeddings in SQLite, and answers semantic queries offline; for example, `rhizome search "pull apart"` surfaces relevant snippets across projects:
rhizome search --limit 2 "pull apart"
Model already exists at \~/.rhizome/models/bge-small-en-v1.5.onnx
\~/Projects/rhizome/src/chunking.rs:458\:fn rust\_no\_structural\_items\_fallback() {
\~/Projects/rhizome/src/lib.rs:2\:pub mod chunking;
jasonjmcghee · 1h ago
Another very cool attribute of embeddings and embedding search is that they are resource cheap enough that you can perform them client side.
You can even build and statically host indices like hnsw for embeddings.
I put together a little open source demo for this here https://jasonjmcghee.github.io/portable-hnsw/ (it's a prototype / hacked together approximation of hnsw, but you could implement the real thing)
Long story short, represent indices as queryable parquet files and use duckdb to query them.
Depending on how you host, it's either free or nearly free. I used Github Pages so it's free. R2 with cloudflare would only cost the size what you store (very cheap- no egress fees).
qq99 · 58m ago
I was wondering about this. I was hesitant to add embedding-based search to my app because I didn't want to incur the latency to the embedding API provider blocking every search on initial render. Granted, you can cache the embeddings for common searches. OTOH, I also don't want to render something without them, perform the embedding async, and then have to reify the results list once the embedding arrives. Seems hard to sensibly do that from a UX perspective.
To render locally, you need access to the model right? I just wonder how good those embeddings will be compared to those from OpenAI/Google/etc in terms of semantic search. I do like the free/instant aspect though
I've had a particularly good experiences with nomic, bge, gte, and all-MiniLM-L6-v2. All are hundreds of MB (except all-minilm which is like 87MB)
simonw · 22m ago
I love all-MiniLM-L6-v2 - 87MB is tiny enough that you could just load it into RAM in a web application process on a small VM. From my experiments with it the results are Good Enough for a lot of purposes. https://simonwillison.net/2023/Sep/4/llm-embeddings/#embeddi...
jbellis · 1h ago
Great to see embeddings getting some love outside the straight-up-ML space!
I had a non-traditional use case recently, as well. I wanted to debounce the API calls I'm making to gemini flash as the user types his instructions, and I decided to try a very lightweight embeddings model, light enough to run on CPU and way too underpowered to attempt vector search with. It works pretty well! https://brokk.ai/blog/brokk-under-the-hood
jacobr1 · 2h ago
I may have missed it ... but were any direct applications to tech writers discussed in this article? Embeddings are fascinating and very important for things like LLMs or semantic search, but the author seems to imply more direct utility.
kaycebasques · 1h ago
> were any direct applications to tech writers discussed in this article
No, it was supposed to be a teaser post followed up by more posts and projects exploring the different applications of embeddings in technical writing (TW). But alas, life happened, and I'm now a proud new papa with a 3-month old baby :D
I do have other projects and embeddings-related posts in the pipeline. Suffice to say, embeddings can help us make progress on all 3 of the "intractable" challengs of TW mentioned here: https://technicalwriting.dev/strategy/challenges.html
jacobr1 · 36m ago
Thanks for sharing regardless. It was a good overview for those less familiar with the material.
PaulHoule · 2h ago
Semantic search and classification and clustering. For the first, there is a substantial breakthrough in IR every 10 years or so you take what you can get. (I got so depressed reading TREC proceedings which seemed to prove that "every obvious idea to improve search relevance doesn't work" and it wasn't until I found a summary of the first ten years that I learned that the first ten years had turned up one useful result, BM2.5)
As for classification, it is highly practical to put a text through an embedding and then run the embedding through a classical ML algorithm out of
This works so consistently that I'm considering not packing in a bag-of-words classifier in a text classification library I'm working on. People who hold court on Huggingface forums tends to believe you can do better with fine-tuned BERT, and I'd agree you can do better with that, but training time is 100x and maybe you won't.
20 years ago you could make bag-of-word vectors and put them through a clustering algorithm
I'd disagree with the bit that it takes "a lot of linear algebra" to find nearby vectors, it can be done with a dot product so I'd say it is "a little linear algebra"
sansseriff · 1h ago
It would be great to semantically search through literature with embeddings. At least one person I know if is trying to generate a vector database of all arxiv papers.
The big problem I see is attribution and citations. An embedding is just a vector. It doesn't contain any citation back to the source material or modification date or certificate of authenticity. So when using embeddings in RAG, they only serve to link back to a particular page of source material.
Using embeddings as links doesn't dramatically change the way citation and attribution are handled in technical writing. You still end up citing a whole paper or a page of a paper.
I think GraphRAG [1] is a more useful thing to build on for technical literature. There's ways to use graphs to cite a particular concept of a particular page of an academic paper. And for the 'citations' to act as bidirectional links between new and old scientific discourse. But I digress
I built an rss aggregator with semantic search using embeddings. The main usage was being able to categorise based on any randomly created category. So you could have arbitrary categories
Unfortunately I no longer work at AWS so the infrastructure that was running it is down.
minimaxir · 1h ago
> I don’t know. After the model has been created (trained), I’m pretty sure that generating embeddings is much less computationally intensive than generating text.
An embedding is generated after a single pass through the model, so functionally it's the equivalent of generating a single token from an text generation model.
energy123 · 1h ago
I might be wrong but aren't embedding models usually bidirectional and not causal, so the attention mechanism itself is more expensive.
minimaxir · 44m ago
It depends on the architecture (you very well can convert a decoder-only causal model to an embeddings model, e.g. Qwen/Mistral), but it is true the traditional embeddings models such as a BERT-based one are bidirectional, although unclear how much more compute that inherently requires.
Semantic search seems like a more promising usecase than simple related articles. A big problem with classical keyword-based search is that synonyms are not reflected at all. With semantic search you can search for what you mean, not what words you expect to find on the site you are looking for.
PaulHoule · 1h ago
A case related to that is "more like this" which in my mind breaks down into two forks:
(1) Sometimes your query is a short document. Say you wanted to know if there were any patents similar to something you invented. You'd give a professional patent searcher a paragraph or a few paragraphs describing the invention, you can give a "semantic search engine" the paragraph -- I helped build one that did about as well as the professional using embeddings before this was cool.
(2) Even Salton's early works on IR talked about "relevance feedback" where you'd mark some documents in your results as relevant, some as irrelevant. With bag-of-words this doesn't really work well (it can take 1000 samples for a bag-of-words classifier to "wake up") but works much better with embeddings.
The thing is that embeddings are "hunchy" and not really the right data structure to represent things like "people who are between 5 feet and 6 feet tall and have been on more than 1000 airplane flights in their life" (knowledge graph/database sorts of queries) or "the thread that links the work of Derrida and Badiou" (could be spelled out logically in some particular framework but doing that in general seems practically intractable)
kgeist · 2h ago
In my benchmarks for a service which is now running in production, hybrid search based on both keywords and embeddings performed the best. Sometimes you need exact keyword matches; other times, synonyms are more useful. Hybrid search combines both sets of results into a single, unified set. OpenSearch has built-in support for this approach.
jbellis · 1h ago
they're both useful
search is an active "I'm looking for X"
related articles is a passive "hey thanks for reading this article, you might also like Y"
podgietaru · 1h ago
I wrote a blog post about embedding - and a sample application to show their uses.
I really enjoy working with embedding. They’re truly fascinating as a representation of meaning - but also a very cheap and effective way to perform very cheap things like categorisation and clustering.
btbuildem · 1h ago
How would you approach using them in a specialized discipline (think technical jargon, acronyms etc) where traning a model from scratch is practically impossible because everyone (customers, solution providers) fiercely guards their data?
A generic embedding model does not have enough specificity to cluster the specialized terms or "code names" of specific entities (these differ across orgs but represent the same sets of concepts within the domain). A more specific model cannot be trained because the data is not available.
Quite the conundrum!
minimaxir · 30m ago
You can fine-tune existing embedding models.
milindsoni · 52m ago
I was using transformer.js to generate and use embeddings with small models in the browser itself, its quite useful to implement any kind of semantic search.
How are they underrated when they have been been used by the top sites for over a decade? The author doesn't really explain why he thinks they are underrated despite them being behind almost every search and recommendation users receive on their computers.
minimaxir · 38m ago
Underrated is more a relative term, and embeddings are definitely underrated to all the other uses for the LLM boom.
I could imagine an LLM inference pipeline where the next token ranking includes its similarity to the target embedding, or perhaps instead the change in direction towards/away from the desired embedding that adding it would introduce.
Put another way, the author gives the example:
> embedding("king") - embedding("man") + embedding("woman") ≈ embedding("queen")
What if you could do that but for whole bodies of text?
I'm imagining being able to do "semantic algebra" with whole paragraphs/articles/books. Instead of just prompting an LLM to "adjust the tone to be more friendly", you could have the core concept of "friendly" (or some more nuanced variant thereof) and "add" it to your existing text, etc.
> I could tell you exactly how I think we might advance the state of the art in technical writing with embeddings, but where’s the fun in that? You now know why they’re such an interesting and useful new tool in the technical writer toolbox… go connect the rest of the dots yourself!
I read the article because of the title, only to find the above.
nit. This suggests that the model contains a direction with some notion of gender, not a dimension. Direction and dimension appear to be inextricably linked by definition, but with some handwavy maths, you find that the number of nearly orthogonal dimensions within n dimensional space is exponential with regards to n. This helps explain why spaces on the order of 1k dimensions can "fit" billions of concepts.
In
https://nlp.stanford.edu/projects/glove/
there are a number of graphs where they have about N=20 points that seem to fall in "the right place" but there are a lot of dimensions involved and with 50 dimensions to play with you can always find a projection that makes the 20 points fall exactly where you want them fall. If you try experiments with N>100 words you go endlessly in circles and produce the kind of inconclusively negative results that people don't publish.
The BERT-like and other transformer embeddings far outperform word vectors because they can take into account the context of the word. For instance you can't really build a "part of speech" classifier that can tell you "red" is an adjective because it is also a noun, but give it the context and you can.
In the context of full text search, bringing in synonyms is a mixed bag because a word might have 2 or 3 meanings and the the irrelevant synonyms are... irrelevant and will bring in irrelevant documents. Modern embeddings that recognize context not only bring in synonyms but the will suppress usages of the word with different meanings, something the IR community has tried to figure out for about 50 years.
In addition to being able to utilize attention mechanisms, modern embedding models use a form of tokenization such as BPE which a) includes punctuation which is incredibly important for extracting semantic meaning and b) includes case, without as much memory requirements as a cased model.
The original BERT used an uncased, SentencePiece tokenizer which is out of date nowadays.
Little did I know that people were going to have a lot of tolerance for "short circuiting" of LLMs, that is getting the right answer by the wrong path, so I'd say now that my methodology of "predictive evaluation" that would put an upper bound on what a system could do was pessimistic. Still I don't like giving credit for "right answer by wrong means" since you can't count on it.
Ramsey theory (or 'the Woolworths store alignment hypothesis')
I think your comment is also clicking for me now because I previously did not really understand how cosine similarity worked, but then watched videos like this and understand it better now: https://youtu.be/e9U0QAFbfLI
I will eventually update the post to correct this inaccuracy, thank you for improving my own wetware's conceptual model of embeddings
So the distinction between a direction and a dimension expressing 'gender' is that maybe gender isn't 'important' (or I guess high-information-density) enough to be an entire dimension, but rather is expressed by a linear combination of two (or more) yet more abstract dimensions.
This is maybe showing some age as well, or maybe not. It seems that text generation will soon be writing top tier technical docs - the research done on the problem with sycophancy will likely result something significantly better than what LLMs had before the regression to sycophancy. Either way, I take "having the biggest impact on technical writing" to mean in the near term. If having great search and organization tools (ambient findability and such) is going to steal the thunder from LLMs writing really good technical docs, it's going to need to happen fast.
https://transformer-circuits.pub/2022/toy_model/index.html
nit within a nit: I believe you intended to write "nearly orthogonal directions within n dimensional space" which is important as you are distinguishing direction from dimension in your post.
For large datasets (as the UMAP algorithm scales in exponential compute), you will need to use the GPU-accelerated UMAP from cuML. https://docs.rapids.ai/api/cuml/stable/api/#umap
The post was previously discussed 6 months ago: https://news.ycombinator.com/item?id=42013762
To be clear, when I said "embeddings are underrated" I was only arguing that my fellow technical writers (TWs) were not paying enough attention to a very useful new tool in the TW toolbox. I know that the statement sounds silly to ML practitioners, who very much don't "underrate" embeddings.
I know that the post is light on details regarding how exactly we apply embeddings in TW. I have some projects and other blog posts in the pipeline. Short story long, embeddings are important because they can help us make progress on the 3 intractable challenges of TW: https://technicalwriting.dev/strategy/challenges.html
More significantly, after having read the first 6 or 8 paragraphs, i still have no clue what an "embedding" is. From the 3rd paragraph:
> Here’s an overview of how you use embeddings and how they work.
But no mention of what they are (unless perhaps it's buried far deeper in the article).
I’m curious how you found the quality of the results? This gets into evals which ML folks love, but even just with “vibes” do the results eyeball as reasonable to you?
Wow, that's bold. I guess "good" technical writing no longer includes a thesis statement.
Seriously though, why would this be useful for technical writing? Sure you could make some similar pages widget however i dont think i've ever wanted that when reading technical docs, let alone writing them.
Embeddings are a _very_ useful tool for building better search - they can handle "fuzzy" matches, where a user can say things like "that feature that lets me run a function against every column of data" because they can't remember the name of the feature.
With embeddings you can implement a hybrid approach, where you mix both keyword search (still necessary because embeddings can miss things that use jargon they weren't trained on) and vector similarity search.
I wish I had good examples to point to for this!
I think there needs to be some more clarification here. Hash functions also return the same sized output no matter how big or small the input text. However, mathematically comparing two hashes is going to have a much different meaning than mathematically comparing two embeddings.
I'd recommend emphasizing that embeddings are training dependent--the quality of comparison will depend on the quality and type of training used to produce the embedding. There isn't some single "universal embedding" that allows for meaningful comparison of arbitrary text.
ONNX models can be loaded and executed with transformer.js https://github.com/huggingface/transformers.js/
You can even build and statically host indices like hnsw for embeddings.
I put together a little open source demo for this here https://jasonjmcghee.github.io/portable-hnsw/ (it's a prototype / hacked together approximation of hnsw, but you could implement the real thing)
Long story short, represent indices as queryable parquet files and use duckdb to query them.
Depending on how you host, it's either free or nearly free. I used Github Pages so it's free. R2 with cloudflare would only cost the size what you store (very cheap- no egress fees).
To render locally, you need access to the model right? I just wonder how good those embeddings will be compared to those from OpenAI/Google/etc in terms of semantic search. I do like the free/instant aspect though
I've had a particularly good experiences with nomic, bge, gte, and all-MiniLM-L6-v2. All are hundreds of MB (except all-minilm which is like 87MB)
I had a non-traditional use case recently, as well. I wanted to debounce the API calls I'm making to gemini flash as the user types his instructions, and I decided to try a very lightweight embeddings model, light enough to run on CPU and way too underpowered to attempt vector search with. It works pretty well! https://brokk.ai/blog/brokk-under-the-hood
No, it was supposed to be a teaser post followed up by more posts and projects exploring the different applications of embeddings in technical writing (TW). But alas, life happened, and I'm now a proud new papa with a 3-month old baby :D
I do have other projects and embeddings-related posts in the pipeline. Suffice to say, embeddings can help us make progress on all 3 of the "intractable" challengs of TW mentioned here: https://technicalwriting.dev/strategy/challenges.html
As for classification, it is highly practical to put a text through an embedding and then run the embedding through a classical ML algorithm out of
https://scikit-learn.org/stable/supervised_learning.html
This works so consistently that I'm considering not packing in a bag-of-words classifier in a text classification library I'm working on. People who hold court on Huggingface forums tends to believe you can do better with fine-tuned BERT, and I'd agree you can do better with that, but training time is 100x and maybe you won't.
20 years ago you could make bag-of-word vectors and put them through a clustering algorithm
https://scikit-learn.org/stable/modules/clustering.html
and it worked but you got awful results. With embeddings you can use a very simple and fast algorithm like
https://scikit-learn.org/stable/modules/clustering.html#k-me...
and get great clusters.
I'd disagree with the bit that it takes "a lot of linear algebra" to find nearby vectors, it can be done with a dot product so I'd say it is "a little linear algebra"
The big problem I see is attribution and citations. An embedding is just a vector. It doesn't contain any citation back to the source material or modification date or certificate of authenticity. So when using embeddings in RAG, they only serve to link back to a particular page of source material.
Using embeddings as links doesn't dramatically change the way citation and attribution are handled in technical writing. You still end up citing a whole paper or a page of a paper.
I think GraphRAG [1] is a more useful thing to build on for technical literature. There's ways to use graphs to cite a particular concept of a particular page of an academic paper. And for the 'citations' to act as bidirectional links between new and old scientific discourse. But I digress
[1] https://microsoft.github.io/graphrag/
https://github.com/aws-samples/rss-aggregator-using-cohere-e...
Unfortunately I no longer work at AWS so the infrastructure that was running it is down.
An embedding is generated after a single pass through the model, so functionally it's the equivalent of generating a single token from an text generation model.
Compare to ModernBERT, which uses more modern techniques and is still bidirectional, but it is very very speedy. https://huggingface.co/blog/modernbert
(1) Sometimes your query is a short document. Say you wanted to know if there were any patents similar to something you invented. You'd give a professional patent searcher a paragraph or a few paragraphs describing the invention, you can give a "semantic search engine" the paragraph -- I helped build one that did about as well as the professional using embeddings before this was cool.
(2) Even Salton's early works on IR talked about "relevance feedback" where you'd mark some documents in your results as relevant, some as irrelevant. With bag-of-words this doesn't really work well (it can take 1000 samples for a bag-of-words classifier to "wake up") but works much better with embeddings.
The thing is that embeddings are "hunchy" and not really the right data structure to represent things like "people who are between 5 feet and 6 feet tall and have been on more than 1000 airplane flights in their life" (knowledge graph/database sorts of queries) or "the thread that links the work of Derrida and Badiou" (could be spelled out logically in some particular framework but doing that in general seems practically intractable)
search is an active "I'm looking for X"
related articles is a passive "hey thanks for reading this article, you might also like Y"
https://aws.amazon.com/blogs/machine-learning/use-language-e...
https://github.com/aws-samples/rss-aggregator-using-cohere-e...
I really enjoy working with embedding. They’re truly fascinating as a representation of meaning - but also a very cheap and effective way to perform very cheap things like categorisation and clustering.
A generic embedding model does not have enough specificity to cluster the specialized terms or "code names" of specific entities (these differ across orgs but represent the same sets of concepts within the domain). A more specific model cannot be trained because the data is not available.
Quite the conundrum!
Very little maths and lots of dogs involved.