dmitrykan

6 ms·

1. 1.▲
   by dmitrykan 9mo ago

   On a Research Assistant: https://muves.io/ It helps to comprehend research papers (and not only papers - any document on any language) faster. The tool is free to use, because we have credits from GCP. I guess at some point
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   by dmitrykan 2y ago

   Leo Boytsov, my guest on the Vector Podcast, made an honest and an eye-opening claim about vector search being intellectually rewarding, but professionally undervalued. What picked my attention was how he gives credit to people who actually
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   The Rise, Fall, and Future of Vector Databases: How to Pick the One That Lasts(dmitry-kan.medium.com)

   1 points by dmitrykan 2y ago|1 comments
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   by dmitrykan 2y ago

   My take on whether we've lost the category of vector databases.
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   by dmitrykan 2y ago

   I'm working on the tool, that includes AI. My original target is to test it on my https://www.youtube.com/c/VectorPodcast by offering something that Lex Fridman does for his episodes. Current features: 1. Download
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   by dmitrykan 3y ago

   https://dmitry-kan.medium.com/ I write about vector search, ANN algorithms, neural search frameworks, search engines and algorithms in general and publish episodes of the Vector Podcast.
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   by dmitrykan 3y ago

   Nice post, thanks for sharing! Learnt about new dbs / solutions for vector search from it. Vicki mentions this survey I wrote some time ago: https://towardsdatascience.com/milvus-pinecone-vespa-weaviat... Hoping it&#x2
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   by dmitrykan 4y ago

   thank you, Doug! It is mutual. Had a fantastic discussion with you on the podcast: https://www.youtube.com/watch?v=Kpua1Euc-B8
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   by dmitrykan 4y ago

   It was great to discuss with Jo Kristian on these topics and more: - History of Vespa - Tensor data structure and its use cases - Multi-stage ranking pipeline - Game-changing vector search in Vespa - Approximate vs exact nearest neighbor se
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    by dmitrykan 4y ago

    Thanks for the article, I've learnt new search engines despite spending a couple of years recently in web scale search. I think you may consider https://usearch.com/ as another dimension in web scale search, where quer
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    by dmitrykan 4y ago

    Great project! Elasticsearch / OpenSearch / Solr have their own learning to rank plugins. Have you considered integrating Metarank with such systems? Or is your vision to provide a reranker layer, that can be independent of the un
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    by dmitrykan 5y ago

    This is fantastic work, Max! I like where you're going with this unit economy. It is increasingly more and more common that companies need ways of productizing their deep learning models with tight SLA constraints and tight budgets. I
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    by dmitrykan 5y ago

    5th episode of Vector Podcast with Tom Lackner, VP Engineering at classic.com explaning the choise of Qdrant vector DB for his projects.
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    by dmitrykan 5y ago

    thanks!
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    by dmitrykan 5y ago

    To learn about vector search and get practical with it, you can check out my blog as well: https://dmitry-kan.medium.com/list/vector-search-e9b564d1427... I'm also hosting a podcast around these topics: https:&#x
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    Semantic search through Wikipedia with the Weaviate vector search engine(github.com)

    13 points by dmitrykan 5y ago|4 comments
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    by dmitrykan 5y ago

    11M+ Wikipedia articles indexed for similarity search.
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    Vector Search(dmitry-kan.medium.com)

    3 points by dmitrykan 5y ago|1 comments
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    by dmitrykan 5y ago

    From code to podcast to billion-scale ANN challenge and statistics
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    by dmitrykan 5y ago

    Thanks! One practical target I imagine having is: 1. write the program in Python. 2. translate it into Ć ("interlingua") 3. compile Ć into Go -- for instance there is a belief around me, that Python is not suitable for web scale l
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    by dmitrykan 5y ago

    Thanks for sharing! The practical value of such languages could be to build an interlingua and support Python->Ć->Go. The question is: if Ć->Python works seamlessly, how easy it is to make the opposite translation? For instance, ta
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    by dmitrykan 5y ago

    A related discussion and a detailed study about vector databases can be found here: https://news.ycombinator.com/item?id=28727816
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    by dmitrykan 5y ago

    Vector Podcast is here to bring you the depth and breadth of Search Engine Technology, Product, Marketing, Business. In the podcast we talk with engineers, entrepreneurs, thinkers and tinkerers, who put their soul into search. Depending on
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    Vector Podcast for Vector Search: people, businesses, products(dmitry-kan.medium.com)

    7 points by dmitrykan 5y ago|2 comments
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    by dmitrykan 5y ago

    sure!
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    by dmitrykan 5y ago

    Yes, for larger datasets of vectors, having the search do a linear scan will likely be slow. So you could take a look at KNN/ANN (K nearest / Approximate nearest neighbors), like https://faiss.ai/ But if you prefe
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    by dmitrykan 5y ago

    Thanks for more detail, this sounds quite interesting. So basically the 16-digit chromosome records you added to each document would work as nearest neighbors? Did this satisfy all your search needs for an algorithm?
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    by dmitrykan 5y ago

    btw, if would be cool to see some example of a genetic algorithm input and resulting vector? And how do you use the vectors in down-stream tasks?
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    by dmitrykan 5y ago

    Such a great question! I think vector search is designed to solve a specific task, like finding K nearest neighbors for your given vector embedding. If you have a few hundred thousand, a million, it might work fine with a linear scan. Again
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    by dmitrykan 5y ago

    Hi! It is a metered post atm, will consider making it more public over time. If you want to get practical with vector search in Solr/Elasticsearch/Opensearch you can try out this repo: https://github.com/DmitryKey&