[HN Gopher] Algolia Acquires Search.io
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Algolia Acquires Search.io
Author : daolf
Score : 207 points
Date : 2022-09-14 07:20 UTC (1 days ago)
(HTM) web link (www.algolia.com)
(TXT) w3m dump (www.algolia.com)
| shermozle wrote:
| Congrats Hamish, Dave, Alex and team!
| O__________O wrote:
| From the Search.io homepage, "we are the only search technology
| supporting full upserts. Your updates are instantly live in
| milliseconds, no matter the scale."
|
| Anyone able to speculate how they were able to achieve this? Or
| for that matter beyond good sales & marketing - what technically
| gave them an edge that market actually needed?
| gk1 wrote:
| This is easily disproved: Pinecone (disclaimer: I work there)
| also supports live index updates... "no matter the scale." I
| think their marketing folks have gone a little far, and I say
| that as a marketing person.
| kartoolOz wrote:
| Even self hosted milvus supports this.
| peterstjohn wrote:
| Heh, my eyes did pop at that one, considering we've also been
| doing that over here since 2020 at least ;)
| ddorian43 wrote:
| > Anyone able to speculate how they were able to achieve this?
|
| vespa.ai does it and it's open source
| justmestanding wrote:
| esher wrote:
| I stopped reading after three words: "Algolia disrupts market
| ...". Goodbye. I am still a fan of DocSearch.
| Beefin wrote:
| I've been interviewing users of vector search and am cataloguing
| my findings in this repo:
|
| https://github.com/esteininger/vector-search
|
| feel free to watch for updates :)
| swyx wrote:
| some interesting points from this other piece
|
| "While the acquisition price was undisclosed, media reports
| suggest Algolia paid more than $100 million for Search.io"
|
| https://www.businessnewsaustralia.com/articles/french-unicor...
|
| "Search.io's mission is to "enable every organization to build
| smart search and discovery solutions." The company was founded in
| 2014 by Hamish Ogilvy and David Howden (originally named Sajari,
| and recently rebranded to Search.io). "
|
| Contra the business news article: "Search.io was founded in 2020
| by Hamish Ogilvy, who will remain with the merged company in the
| new role of vice president of artificial intelligence."
|
| ---
|
| alright can some non marketing person explain in practical
| usecases why this "hybrid search" is so disruptive? i feel like
| the article is trying really hard to communicate how big a deal
| it is, but it falls flat on me because i simply only have
| pedestrian search knowledge
| peterstjohn wrote:
| It really does give you the best of both worlds - resistant to
| typos, handling synonyms without all the usual hand-written
| rules, but still able to handle direct searches like ISBNs.
|
| (disclaimer: I work on Semantic Search at Lucidworks)
| dustincoates wrote:
| Vector search is incredibly powerful on matching on context or
| similarity. For example, automobile and car are semantically
| similar and, and one will rank well for the other in a search.
|
| Vector search, though, isn't as good on handling typos and not
| good at all when it comes to as you type searching. Vehic won't
| match on auto, for example.
|
| We believe that there is use for each of these approaches and a
| use in a single search, rather than choosing ahead of time or
| through heuristics after the fact which to choose.
|
| (I'm a Principal PM for Semantic Search and Search Ranking at
| Algolia.)
| tomrod wrote:
| Do yall have a technical blog? I would love to both
| understand the problem and methods, domains yall cross (eg
| biometrics and fuzzy matching?), and how yall integrate in
| different industries.
|
| A good search partner is hard to find. PageRank is fun and
| all, but I believe better methods exist these days.
| dustincoates wrote:
| Here's a link to our engineering blog posts:
| https://www.algolia.com/blog/engineering/
|
| And our CTO, Julien, wrote an "Inside the Engine" series on
| how our search engine works. It doesn't have the new
| "hybrid search" but it shows you the base of how we do
| search: https://www.algolia.com/blog/engineering/inside-
| the-algolia-...
| [deleted]
| marcinzm wrote:
| There's two related problems here: finding relevant results
| and ranking those results. The first is historically done
| with massive inverted indexes. Page rank is for the second
| one of ranking those relevant results.
|
| For the first part you can look into "embeddings" and
| "approximate nearest neighbor lookup" for the modern
| approaches. That said inverted indexes are still very
| popular.
|
| The second one is generally called "learning to rank" so
| you can find a lot of things written on that topic. The
| biggest issue here imho is what training data you use which
| gives you examples of good rankings. The best algorithm
| trained on garbage will give you garbage.
| fzliu wrote:
| I'd like to add that vector search works not just for natural
| language, but also for a variety of other types of
| unstructured data as well. Images, video, user profiles, and
| pretty much anything else that can be vectorized. Here's an
| example of image search:
| https://milvus.io/docs/image_similarity_search.md
| kartoolOz wrote:
| These are relatively easy to build and can be used for a
| variety of tasks like Entity Resolution,
| https://news.ycombinator.com/item?id=32825679
| marcinzm wrote:
| Aren't typos just a question of how you generate your
| vectors/embeddings? I'd be surprised if a transformer with a
| character level tokenizer trained on a representative source
| of data (ie: with typos) wouldn't be able to make sense of
| typos.
| evrydayhustling wrote:
| Can confirm. We use sentence-level transformer embeddings
| for (vector) search, clustering, and classification tasks.
| As an old school ML guy I've been amazed at how robust they
| are to typos, slang, punctuation, etc.
|
| However, I'm sure there are still applications where you
| don't have access to a robust embedding for your domain but
| can apply other techniques to deal with that domain's
| noise.
| O__________O wrote:
| Here is decent intro to sentence level transformers &
| embeddings:
|
| https://www.pinecone.io/learn/sentence-embeddings/
| dustincoates wrote:
| Yes, good point. I still believe that net-net you're going
| to get better results on typos with a keyword-based search,
| but I didn't mean to imply that vector searching won't
| handle typos at all.
| dnc wrote:
| > Vector search, though, isn't as good on handling typos and
| not good at all when it comes to as you type searching. Vehic
| won't match on auto, for example.
|
| This is incorrect in general case and it entirely depends on
| the model that is used to produce word vectors and the text
| corpus the model is trained with.
|
| For instance, fastText model is trained on words, but also
| their parts (n-grams), so it should produce word vectors that
| would be close (in cosine-distance) to vectors of their
| corresponding typos and partials, even if the text corpus
| that was used to train the model doesn't contain same typos
| and partially typed words verbatim.
| jabo wrote:
| Note: I work on Typesense [1], an open source alternative to
| Algolia.
|
| This is an interesting acquisition from my perspective because we
| also just started working on adding vector search to Typesense
| about a month ago.
|
| So you can now do nearest neighbor searches by bringing your
| vectors into Typesense. This lets you do things like similarity
| searches, recommendations, etc.
|
| I'd love to have more beta testers use the feature and give us
| feedback. If you'd like to try it out, please send me an email:
| jasonb at typesense dOt org
|
| In any case, congratulations Search.io / Sajari team!
|
| [1] https://typesense.org/
| theallan wrote:
| Just wanted to say thank you for Typesense. I use it for my own
| open source software [1] and integration with it was really
| easy.
|
| More generally, I think it is great to see development in this
| area from Algolia and Search.io to Typesense and others. Being
| able to have a customisable search which is really fast, can
| make a bit difference on a web-site.
|
| [1] https://datatables.net
| slivanes wrote:
| Thank you for building DataTables - very useful web software.
| jabo wrote:
| Thank you for saying that and thank _you_ for building
| DataTables!
|
| I've heard about DataTables in various contexts over the
| years, so it's really cool to hear that you've integrated it
| with Typesense.
| slig wrote:
| Thank you very much for working on Vector Search. Looking
| forward trying it out as soon as it gets stable. Thanks again!
| flurly wrote:
| Typesense is so good (and affordable!). I'm so thankful y'all
| are providing an alternative to Algolia.
|
| It feels similar to Render vs Vercel where clearly players like
| Vercel & Algolia make the big bucks from enterprise clients and
| thus make their services less accessible to small companies
| like mine.
|
| Quick question with regards to vector search, do y'all intend
| on exposing some basic embedding service to your platform? I
| think it'd be pretty powerful to add a basic word2vec embedding
| model so that users who want to play around with vector search
| can simply just send some text and typesense would do the rest
| (convert text to embedding, index embedding, etc).
| karterk wrote:
| Yes, we plan to do that, but we will start off with first
| supporting raw vector data type and search on that.
| bilalq wrote:
| We're currently A/B testing TypeSense and Algolia, but the
| pricing model difference alone makes me almost want to skip the
| whole process and just go with TypeSense. Algolia's price per
| search model is a little ridiculous for people building UIs
| with live search.
| chairmanwow1 wrote:
| This article is tripping over itself to tell you how great they
| are.
|
| PR like this just feels like it was written by a college kid or
| makes me feel like they are compensating for technical
| inadequacy.
|
| No thanks.
| Cthulhu_ wrote:
| It's not written for technical people, it's for business,
| management, financial people, the SEC and (potential)
| investors.
| dsmmcken wrote:
| Does this improve or impact docsearch?
| csmpltn wrote:
| > "... both keyword and semantic search in a single API. This new
| API platform is blazing fast, massively scalable, and,
| importantly, cost effective. No other vendor offers this today."
|
| Where's the proof that "no other vendor offers this today"?
| julienfr112 wrote:
| What about the competition on the relevant market ?
| donretag wrote:
| Here is a comparison of various dense vector solutions:
| https://dmitry-kan.medium.com/how-to-choose-a-vector-databas...
|
| What is missing is Licence's implemention, which helps power
| Solr/OpenSearch/Elasticsearch
| mrwnmonm wrote:
| These days, I get to know new services only when they get
| acquired.
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(page generated 2022-09-15 23:02 UTC)