8 ms·
32k context length text embedding models
- johnfn 2y agoWhat on earth is "OpenAI V3"? Just to be sure I wasn't being obtuse, I Googled it, only to get a bunch of articles pointing back at this post.
- chipgap98 2y agoIt is OpenAI's vector embedding model
- refulgentis 2y agohttps://openai.com/index/new-embedding-models-and-api-updates/ https://openai.com/index/new-embedding-models-and-api-update... API constant is text-embedding-3
- qeternity 2y agoYou missed the “large” which adds context: “OpenAI V3 large” which is their sota large embedding model.
- throwup238 2y agoWhat’s the benefit of generating embeddings for such large chunks? Do people use these large contexts to include lots of document specific headers/footers or are they actually generating embeddings of single large documents? I don’t understand how the math works out on those vectors
- Arctic_fly 2y ago> What’s the benefit of generating embeddings for such large chunks? Not an expert, but I believe now that we can fit more tokens into an LLM's context window, we can avoid a number of problems by providing additional context around any chunk of text that might be useful to the LLM. Solves the problem of misinterpretation of the important bit by the LLM.
- lsorber 2y agoYou don’t have to reduce a long context to a single embedding vector. Instead, you can compute the token embeddings of a long context and then pool those into say sentence embeddings. The benefit is that each sentence’s embedding is informed by all of the other sentences in the context. So when a sentence refers to “The company” for example, the sentence embedding will have captured which company that is based on the other sentences in the context. This technique is called ‘late chunking’ [1], and is based on another technique called ‘late interaction’ [2]. And you can combine late chunking (to pool token embeddings) with semantic chunking (to partition the document) for even better retrieval results. For an example implementation that applies both techniques, check out RAGLite [3]. [1] https://weaviate.io/blog/late-chunking https://weaviate.io/blog/late-chunking [2] https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/ https://jina.ai/news/what-is-colbert-and-late-interaction-an... [3] https://github.com/superlinear-ai/raglite https://github.com/superlinear-ai/raglite
- visarga 2y agoYou can achieve the same effect by using LLM to do question answering prior to embedding, it's much more flexible but slower, you can use CoT, or even graph rag. Late chunking is a faster implicit alternative.
- voiper1 2y agoI read both those articles, but I still don't get how to do it. It seems the idea is that more of the embedding is informed by context, but do I _do_ late chunking? My best guess so far is that somehow I embed a long text and then I break up the returned embedding into multiple parts and search each separately? But that doesn't sound right.
- _hl_ 2y agoYou’d need to go a level below the API that most embedding services expose. A transformer-based embedding model doesn’t just give you a vector for the entire input string, it gives you vectors for each token. These are then “pooled” together (eg averaged, or max-pooled, or other strategies) to reduce these many vectors down into a single vector. Late chunking means changing this reduction to yield many vectors instead of just one.
- voiper1 2y agoI thought embedding large chunks would "dilute" the ideas, since large chunks tend to have multiple disparate ideas? Does it somehow capture _all_ of the ideas, and querying for a single one would somehow match? Isn't that the point of breaking down into sentences? Someone mentioned adding context -- but doesn't it calculate embedding on the whole thing? The API Docs list `input` but no separate `context`. https://docs.voyageai.com/reference/embeddings-api https://docs.voyageai.com/reference/embeddings-api
- OutOfHere 2y agoI would like to see an independent benchmark.
- benreesman 2y agoThis looks quite serious (which would be unsurprising given that Fei-Fei Li and Christopher Ré are involved). I'm also quite interested in the nuts and bolts: does anyone know what the current accepted leaderboard on this is? I was screwing around with GritLM [1] a few months back and I seem to remember the MTEB [2] was kind of the headline thing at that time, but I might be out of date. [1] https://arxiv.org/pdf/2402.09906 https://arxiv.org/pdf/2402.09906 [2] https://huggingface.co/blog/mteb https://huggingface.co/blog/mteb
- ldjkfkdsjnv 2y agoI build a RAG system with voyage and it crushed openai embeddings, the difference in retrieval quality was noticeable
- Oras 2y agoWhat evaluation metrics did you use?
- ldjkfkdsjnv 2y agoNone, I just tested it over and over, with 100 users.
- ChrisArchitect 2y agohttps://hn.algolia.com/?q=https%3A%2F%2Fblog.voyageai.com%2F2024%2F09%2F18%2Fvoyage-3%2F https://hn.algolia.com/?q=https%3A%2F%2Fblog.voyageai.com%2F...
- albert_e 2y agovery interesting observation so the same link has been posted ~10 times in the last one month? and this is the first time the post got any attention mixed feelings there
- Oras 2y agoNot related, but why they don’t have a pricing page? Last time I checked voyageai I had to google their pricing to find the page as it’s not in the nav menu.
- dtjohnnyb 2y agoI've found good results from summarizing my documents using a large context model then embedding those summaries using a standard embedding model (e.g. e5) This way I can tune what aspects of the doc I want to focus retrieval on, it's easier to determine when there are any data quality issues that need to be fixed, and the summaries have turned out to be useful for other use cases in the company.
- tinyhouse 2y agoAgreed. Esp if you gonna call an API, you can call something cheaper than this embeddings model, like 4o-mini, summarize, then use a small embeddings model fine-tuned for your needs locally. I was critical about these guys before (not about their quality of work but rather about building a business around embeddings). This work though seems interesting and I might even give it a try, esp if they provide a fine-tuning API (is that on the roadmap?)
- albert_e 2y agoRelated question: One year ago simonw said this in a post about embeddings: [https://news.ycombinator.com/item?id=37985489 https://news.ycombinator.com/item?id=37985489] > Lots of startups are launching new “vector databases”—which are effectively databases that are custom built to answer nearest-neighbour queries against vectors as quickly as possible. > I’m not convinced you need an entirely new database for this: I’m more excited about adding custom indexes to existing databases. For example, SQLite has sqlite-vss and PostgreSQL has pgvector. Do we still feel specialized vector databases are an overkill? We have AWS promoting amazon OpenSearch as the default vector database for a RAG knowledge base and that service is not cheap. Also I would like to understand a bit more about how to pre-process and chunk the data properly in a way that optimizes the vector embeddings, storage and retrieval ... any good guides on the same i cna refer to? Thanks!
- marcyb5st 2y agoWill try to respond in order: 1. It depends on how much embeddings we are talking about. Few millions, probably yes, 100s millions/Billions range? You likely need something custom. 2. Vectors are only one way to search for things. If your corpus contains stuff that don't carry semantic weight (think about part numbers) and you want to find the chunk that contains that information you'll likely need something that uses tf-idf. 3. Regarding chunk size, it really depends on your data and the queries your users will do. The denser the content the smaller the chunk size. 4. Preprocessing - again, it depends. If it's PDFs with just texts, try to remove footers / headers from the extracted text. Of it contains tables, look at something like table former to extract a good html representation. Clean up other artifacts from the text (like dashes for like breaking, square brackets with reference numbers for scientific papers, ... ).
- dotinvoke 2y agoI had the same idea, but now I a Postgres database that has very high latency for simple queries because the CPU is busy building large HNSW indexes. My impression is that it might be best to do vector index construction separately from the rest of the data, for performance reasons. It seems vector indexes are several orders of magnitude more compute intensive than most other database operations.
- antirez 2y agoI wonder if random projections or other similar dimensionality reduction techniques work equally well then using a model specialized in smaller embeddings capturing the same amount of semantical information. This way we could use larger embeddings of open models working very well and yet enjoy faster node to node similarity during searches.