fzliu

41 ms·

811.
by fzliu 4y ago
You'll have to manually compute the mean after retrieving the nearest neighbors. Automatic fast averaging could be an interesting use case for clustering or using a vector database to generate training though.
812.
by fzliu 4y ago
Not yet, but this functionality should be coming soon. We're currently working on adding the capability to call third party embedding APIs directly from a Zilliz Cloud instance.
813.
by fzliu 4y ago
I work at Zilliz ( https://zilliz.com ) and am a part of the Milvus community. Here are some other resources in case anybody's interested in learning more about embeddings, vector search, and vector databases: 1) Embedding cr
814.
by fzliu 4y ago
If you have a small number of fixed documents e.g. <100k or so, then I agree that pickling the vectors or storing them as bytearrays would work better. Once you reach a certain scale, it's helpful to potentially use distributed quer
815.
by fzliu 4y ago
+1, it's great to see OpenAI being active on the open source side of things (I'm from the Milvus community https://milvus.io ). In particular, the vector stores allow the ability to inject domain knowledge as a prompt i
816.
Jensen Huang: Vector Databases Are Essential for Unlocking LLMs(youtube.com)
5 points by fzliu 4y ago|1 comments
817.
GPU-Accelerated Vector Database(zilliz.com)
4 points by fzliu 4y ago|0 comments
818.
by fzliu 4y ago
One way to get around context length is to perform embedding and retrieval of your entire corpus. Langchain ( https://langchain.readthedocs.io/en/latest/ ) and Milvus ( https://milvus.io ) is one of the st
819.
by fzliu 4y ago
Would like to bring up LangChain as well : https://langchain.readthedocs.io/en/latest/ . We recently integrated Milvus ( https://milvus.io ) into LangChain, so you'll be able to store and process bi
820.
Vector Index Basics and the Inverted File Index(zilliz.com)
3 points by fzliu 4y ago|0 comments
821.
by fzliu 4y ago
Here's a demo using Milvus if you're interested: https://molsearch.milvus.io/ .
822.
by fzliu 4y ago
Vector databases in general are good for storing large amounts of unstructured data by first converting them into embeddings via ML models. There's also feature stores, which store and organize features for later use in model training
823.
Can your phone tell if a bridge is in good shape? (2022)(news.mit.edu)
3 points by fzliu 4y ago|1 comments
824.
Lawsuits pile up as U.S. parents take on social media giants(context.news)
3 points by fzliu 4y ago|0 comments
825.
by fzliu 4y ago
Good stuff! Just out of curiosity, what vector database are you using.
826.
Lessons Learned from 1 Year of Intermittent Fasting(jamesclear.com)
52 points by fzliu 4y ago|46 comments
827.
by fzliu 4y ago
First time I've heard of pgvector - for folks with experience, how does it compare to other ANN plugins (i.e. Redis https://redis.io/docs/stack/search/reference/vectors/ ) and purpose-built vect
828.
by fzliu 4y ago
> The PY32F030 parts advertise a typical low-power stop mode current consumption of 4.5 µA when using a 1.0V setting with the internal regulator, and I was able to get the part to drop down to 5 µA in my testing. Meanwhile, the STM32F030
829.
by fzliu 4y ago
Agreed 100%. Folks often forget that a great model is only a tiny part of the equation - strong execution and a solid demo are also critical. Google/DeepMind might have an RL-based chatbot equal to or even better than ChatGPT, but Open
830.
by fzliu 4y ago
Related: https://milvus.io/milvus-demos/reverse-image-search/ [I'm a part of the Milvus community.]
831.
Tech Lead Management roles are a trap (2020)(lethain.com)
3 points by fzliu 4y ago|0 comments
832.
by fzliu 4y ago
Might be usable for low column count tabular data, but it would be pretty terrible for any other semantically dense modality e.g. video, molecules, geospatial, etc.
833.
by fzliu 4y ago
If you sprinkle in a bit of infrastructure, I think we're already there. The ability to distill a variety of content into vectors and perform approximate nearest neighbor search (shameless plug: https://milvus.io ) across al
834.
A stack of feed-forward layers does surprisingly well on ImageNet(arxiv.org)
97 points by fzliu 4y ago|25 comments
835.
by fzliu 4y ago
This feels like it was written by someone who has been writing C code their entire life.
836.
Why TensorFlow for Python is dying a slow death(thenextweb.com)
2 points by fzliu 4y ago|0 comments
837.
Frank Slootman on leadership and the war against mediocrity(iconiqcapital.com)
1 points by fzliu 4y ago|1 comments
838.
Transformer Reinforcement Learning(github.com)
3 points by fzliu 4y ago|0 comments
839.
by fzliu 4y ago
> This is because when the power supply is first powering up, even if it only takes a second or two, the CPU has already received "dirty" power, because the power supply was building up a steady stream of electricity. Digital l
840.
by fzliu 4y ago
As someone who uses a multitude of workplace apps myself, this is amazing. What kind of model are you using, and do you have plans to provide a service based off this? Good stuff!

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