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Algolia Acquires Search.io

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Re: Algolia Acquires Search.io

#3

Post by swyx »

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."

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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

Re: Algolia Acquires Search.io

#4

Post by dustincoates »

swyx wrote: post #3

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 Sajar…

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.)

Re: Algolia Acquires Search.io

#6

Post by marcinzm »

dustincoates wrote: post #4
swyx wrote: post #3

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 Sajar…

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 singl…

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.

Re: Algolia Acquires Search.io

#7

Post by evrydayhustling »

marcinzm wrote: post #6
dustincoates wrote: post #4

Earlier quoted context omitted.

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 singl…

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.

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.

Re: Algolia Acquires Search.io

#8

Post by dustincoates »

marcinzm wrote: post #6
dustincoates wrote: post #4

Earlier quoted context omitted.

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 singl…

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.

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.

Re: Algolia Acquires Search.io

#10

Post by O__________O »

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?

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