awesome-vector-search
google-research
awesome-vector-search | google-research | |
---|---|---|
20 | 98 | |
1,275 | 32,863 | |
2.5% | 0.9% | |
6.1 | 9.6 | |
22 days ago | 3 days ago | |
Jupyter Notebook | ||
MIT License | Apache License 2.0 |
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awesome-vector-search
- Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
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Reality check on good embedding model (and this idea in general)
Probably. But there are a number of free open source ones. For example, I've got a document that I'm doing embedding-keys for that has about 8000 sentences. Here's a list of some [ https://github.com/currentslab/awesome-vector-search ]
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Rye, meet GPT3 ... and vice versa :)
note: search for vector databases not written in Go but with Go clients, in case there is anything more local/lightweight: https://github.com/currentslab/awesome-vector-search
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Vector database built for scalable similarity search
https://github.com/currentslab/awesome-vector-search
I was surprised to see Elastic actually has ok support for some of this stuff, though it appears slower for most of the tasks.
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Supporting sequence of vectors does seems like a fresh air to the vector search service. I have added marqo to the list of awesome vector search (disclosure: I am the maintainer of the list) to increase your exposure.
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What are vector search engines?
If you want a proper curated list of various libraries and standalone services of vector search engines, refer to this awesome GitHub repository by Currents API.
- List of vector search libraries
- List of curated vector search libraries
- A GitHub repository that collects awesome vector search framework/engine, library, cloud service, and research papers
- Find anything fast with Google's vector search technology
google-research
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Show HN: Next-token prediction in JavaScript – build fast LLMs from scratch
People on here will be happy to say that I do a similar thing, however my sequence length is dynamic because I also use a 2nd data structure - I'll use pretentious academic speak: I use a simple bigram LM (2-gram) for single next-word likeliness and separately a trie that models all words and phrases (so, n-gram). Not sure how many total nodes because sentence lengths vary in training data, but there are about 200,000 entry points (keys) so probably about 2-10 million total nodes in the default setup.
"Constructing 7-gram LM": They likely started with bigrams (what I use) which only tells you the next word based on 1 word given, and thought to increase accuracy by modeling out more words in a sequence, and eventually let the user (developer) pass in any amount they want to model (https://github.com/google-research/google-research/blob/5c87...). I thought of this too at first, but I actually got more accuracy (and speed) out of just keeping them as bigrams and making a totally separate structure that models out an n-gram of all phrases (e.g. could be a 24-token long sequence or 100+ tokens etc. I model it all) and if that phrase is found, then I just get the bigram assumption of the last token of the phrase. This works better when the training data is more diverse (for a very generic model), but theirs would probably outperform mine on accuracy when the training data has a lot of nearly identical sentences that only change wildly toward the end - I don't find this pattern in typical data though, maybe for certain coding and other tasks there are those patterns though. But because it's not dynamic and they make you provide that number, even a low number (any phrase longer than 2 words) - theirs will always have to do more lookup work than with simple bigrams and they're also limited by that fixed number as far as accuracy. I wonder how scalable that is - if I need to train on occasional ~100-word long sentences but also (and mostly) just ~3-word long sentences, I guess I set this to 100 and have a mostly "undefined" trie.
I also thought of the name "LMJS", theirs is "jslm" :) but I went with simply "next-token-prediction" because that's what it ultimately does as a library. I don't know what theirs is really designed for other than proving a concept. Most of their code files are actually comments and hypothetical scenarios.
I recently added a browser example showing simple autocomplete using my library: https://github.com/bennyschmidt/next-token-prediction/tree/m... (video)
And next I'm implementing 8-dimensional embeddings that are converted to normalized vectors between 0-1 to see if doing math on them does anything useful beyond similarity, right now they look like this:
[nextFrequency, prevalence, specificity, length, firstLetter, lastLetter, firstVowel, lastVowel]
- Google Research website is down
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Jpegli: A New JPEG Coding Library
The change was literally just made: https://github.com/google-research/google-research/commit/4a...
It appears this was in response to Hacker News comments.
- Multi-bitrate JPEG compression perceptual evaluation dataset 2023
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Vector Databases: A Technical Primer [pdf]
There are options such as Google's ScaNN that may let you go farther before needing to consider specialized databases.
https://github.com/google-research/google-research/blob/mast...
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Labs.Google
I feel it was unnecesary to create this because https://research.google/ already exists? It just seems like they want to take another URL with a "pure" domain name instead of psubdirectories, etc parts.
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Smerf: Streamable Memory Efficient Radiance Fields
https://github.com/google-research/google-research/blob/mast...
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Shisa 7B: a new JA/EN bilingual model based on Mistral 7B
You could also try some dedicated translation models like https://huggingface.co/facebook/nllb-moe-54b (or https://github.com/google-research/google-research/tree/master/madlad_400 for something smaller) and see how they do.
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Translate to and from 400+ languages locally with MADLAD-400
Google released T5X checkpoints for MADLAD-400 a couple of months ago, but nobody could figure out how to run them. Turns out the vocabulary was wrong, but they uploaded the correct one last week.
- Mastering ROUGE Matrix: Your Guide to Large Language Model Evaluation for Summarization with Examples
What are some alternatives?
pgvector - Open-source vector similarity search for Postgres
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
fast-soft-sort - Fast Differentiable Sorting and Ranking
faiss - A library for efficient similarity search and clustering of dense vectors.
Milvus - A cloud-native vector database, storage for next generation AI applications
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
struct2depth - Models and examples built with TensorFlow