6 ms·

Algolia Acquires Search.io

226 points by daolf 4y ago

chairmanwow1 4y ago
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_ 4y ago
  It's not written for technical people, it's for business, management, financial people, the SEC and (potential) investors.
shermozle 4y ago
Congrats Hamish, Dave, Alex and team!
swyx 4y ago
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-unicorn-algolia-acquires-australian-startup-search-io-to-create--blazing-fast--search-engine.html 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
- dustincoates 4y ago
  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.)
  - marcinzm 4y ago
    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 4y ago
      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 4y ago
        Here is decent intro to sentence level transformers & embeddings: https://www.pinecone.io/learn/sentence-embeddings/ https://www.pinecone.io/learn/sentence-embeddings/
    - dustincoates 4y ago
      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.
julienfr112 4y ago
What about the competition on the relevant market ?
- donretag 4y ago
  Here is a comparison of various dense vector solutions: https://dmitry-kan.medium.com/how-to-choose-a-vector-database-8c6e6f0f8f8b https://dmitry-kan.medium.com/how-to-choose-a-vector-databas... What is missing is Licence's implemention, which helps power Solr/OpenSearch/Elasticsearch
- Beefin 4y ago
  working on a comparison table: https://github.com/esteininger/vector-search/tree/master/foundations/vector-search-comparisons https://github.com/esteininger/vector-search/tree/master/fou...
O__________O 4y ago
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 4y ago
  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 4y ago
    Even self hosted milvus supports this.
    - mish15 4y ago
      On hybrid indexes with full text and vector support?
  - peterstjohn 4y ago
    Heh, my eyes did pop at that one, considering we've also been doing that over here since 2020 at least ;)
  - mish15 4y ago
    Last I heard Pinecone doesn’t even support full text search, let alone hybrid indexes, what do you think you are disproving exactly? Real-time upserts on hybrid and vector indexes is very unusual, please link to how you do this.
- ddorian43 4y ago
  > Anyone able to speculate how they were able to achieve this? vespa.ai does it and it's open source
csmpltn 4y ago
> "... 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"?
justmestanding 4y ago
esher 4y ago
I stopped reading after three words: "Algolia disrupts market …". Goodbye. I am still a fan of DocSearch.
jabo 4y ago
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/ https://typesense.org/
- theallan 4y ago
  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 https://datatables.net
  - slivanes 4y ago
    Thank you for building DataTables - very useful web software.
  - jabo 4y ago
    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.
- flurly 4y ago
  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).
mrwnmonm 4y ago
These days, I get to know new services only when they get acquired.
dsmmcken 4y ago
Does this improve or impact docsearch?
Beefin 4y ago
I've been interviewing users of vector search and am cataloguing my findings in this repo: https://github.com/esteininger/vector-search https://github.com/esteininger/vector-search feel free to watch for updates :)
tullie 4y ago
Congrats to the team at Search.io! Looking forward to seeing what Algolia does with vector search.

226 points by daolf 4y ago

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_ 4y ago
It's not written for technical people, it's for business, management, financial people, the SEC and (potential) investors.

shermozle 4y ago

Congrats Hamish, Dave, Alex and team!

swyx 4y ago

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-unicorn-algolia-acquires-australian-startup-search-io-to-create--blazing-fast--search-engine.html 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

dustincoates 4y ago
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.)
- marcinzm 4y ago
  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 4y ago
    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 4y ago
      Here is decent intro to sentence level transformers & embeddings: https://www.pinecone.io/learn/sentence-embeddings/ https://www.pinecone.io/learn/sentence-embeddings/
  - dustincoates 4y ago
    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.

julienfr112 4y ago

What about the competition on the relevant market ?

donretag 4y ago
Here is a comparison of various dense vector solutions: https://dmitry-kan.medium.com/how-to-choose-a-vector-database-8c6e6f0f8f8b https://dmitry-kan.medium.com/how-to-choose-a-vector-databas... What is missing is Licence's implemention, which helps power Solr/OpenSearch/Elasticsearch
Beefin 4y ago
working on a comparison table: https://github.com/esteininger/vector-search/tree/master/foundations/vector-search-comparisons https://github.com/esteininger/vector-search/tree/master/fou...

O__________O 4y ago

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 4y ago
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 4y ago
  Even self hosted milvus supports this.
  - mish15 4y ago
    On hybrid indexes with full text and vector support?
- peterstjohn 4y ago
  Heh, my eyes did pop at that one, considering we've also been doing that over here since 2020 at least ;)
- mish15 4y ago
  Last I heard Pinecone doesn’t even support full text search, let alone hybrid indexes, what do you think you are disproving exactly? Real-time upserts on hybrid and vector indexes is very unusual, please link to how you do this.
ddorian43 4y ago
> Anyone able to speculate how they were able to achieve this? vespa.ai does it and it's open source

csmpltn 4y ago

> "... 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"?

justmestanding 4y ago

esher 4y ago

I stopped reading after three words: "Algolia disrupts market …". Goodbye. I am still a fan of DocSearch.

jabo 4y ago

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/ https://typesense.org/

theallan 4y ago
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 https://datatables.net
- slivanes 4y ago
  Thank you for building DataTables - very useful web software.
- jabo 4y ago
  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.
flurly 4y ago
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).

mrwnmonm 4y ago

These days, I get to know new services only when they get acquired.

dsmmcken 4y ago

Does this improve or impact docsearch?

Beefin 4y ago

I've been interviewing users of vector search and am cataloguing my findings in this repo: https://github.com/esteininger/vector-search https://github.com/esteininger/vector-search feel free to watch for updates :)

tullie 4y ago

Congrats to the team at Search.io! Looking forward to seeing what Algolia does with vector search.