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Disclaimer: I work at Algolia (https://www.algolia.com/), a hosted search as a service API.

While I agree that building a great and relevant search experience with a Lucene-based engine requires lots of extra time and effort to get right, there are other non-TFIDF based solutions that provide a much faster path to great relevance with far less effort (https://blog.algolia.com/inside-the-algolia-engine-part-1-in...), and it's possible to have semantic ranking without too much machine learning (https://blog.algolia.com/promote-search-results-query-rules/). Not to discount the value of machine learning - we're finding that for specific usecases ML can be a very valuable way to help surface more pertinent content for individuals based on their profile/preferences etc. (https://blog.algolia.com/personalization-announcement/).

This may be along the lines of what you mentioned around "commoditizing" complex traditional search workflows. I'd be curious to hear more about what kind of use-cases you think are trickiest without SS.

Although I'm a big fan of Algolia search (because it's freakin' fast) I happen to know little to nothing of your search model other than what I have learned from Algolians chipping in right here on HN.

I used to be quite impressed with Lucene, even at version 1.0 (when a fuzzy search meant a full table scan), then watched in joy when they conquered the search market, before realizing how it struggled (and still does) with, well, I hate to say it (because I'm usually ridiculed when I bring this up), y'know, big (-ish) data. The proposed and popular solution: sharding the data onto a cluster of machines.

Algolia seems to be a focused, streamlined and more efficient ElasticSearch, at least in the FTS use case.

I've worked almost exclusively in e-com for ~20 years. Algolia FTS+personalization seems to fit the e-commerce use case pretty darn well.

I wonder, regarding "Algolia Query Rules" (which also seems like a real killer-feature for e-commerce):

>> automatically transforming a query word into its equivalent filter (“cheap” would become a filter price< 400...

How do you translate "cheap" into "price<400"? By maintaining a dictionary? Also, what if some people think 400 is quite expensive?

I want to build or implement a search engine that is inherently self-maintained in the same way you and me are self-maintained. As humans, however, we do have a serious flaw. In order for us to maintain an index of our knowledge we need to sleep. To start with I'd like to try to mimic that construct, then move past it.

"Today none of that is relevant. Today you need to provide semantic search ("SS") or your search will be considered broken because the results will truly suck."

I agree semantic search is useful, but what you're proposing sounds vague, like black box magic.

To provide semantic search, you still do the dirty things you just mentioned: integrate a stemming library, integrate synonyms and a huge corpus of 2+ words topics (ie. when someone searches for "big data", you should always return documents with those terms together, and never apart). You need those things. ML might help you generate them, but it isn't magically going to tell you X, Y and Z documents should also be returned for a query, even though it doesn't contain that term.

> SS through ML is commonly a nasty, duct-taped work-flow that at best results in a system that looks more like a POC or DEMO than a proper production system, unless you work at Bing/Google that is (probably, but I have no hands-on experience of those systems).

This is being worked on in Solr community. See an in-progress book: https://www.manning.com/books/ai-powered-search And related issue for an example of work: https://issues.apache.org/jira/browse/SOLR-9418

I've worked in well-known tech companies, and full-text search is very much alive and well. Sure, semantic search is obviously better. But you immediately add a ton of value to many use cases just with FTS. And upgrading to SS is, in several cases, not important enough to warrant the complexity.

> What a typical back-ender would wip up in a couple of days using ES/Solr you now need a proper Math Dev to do and for the MD's model to become useful you need them to hand it over to a distributed systems expert.

This in itself is a good enough reason to be a sufficient argument to refute your main claim that FTS is dead. It will not be dead until an alternative is sufficiently commoditized so that regular backenders can set it up. It may be edged out of certain use cases where it is mission critical to get fantastic results, but there are MANY places of use where good old boring FTS is plenty good enough. Otherwise it wouldn't have been used in the first place.

FTS is fine for so many applications. Many many business cases are solved by elastic search in a couple of days with no need to use ML.

I agree with you for the most part. There are companies providing an extensible and open source platform for incorporating models into production systems and even helping you build those models, like Lucidworks (my employer). Algolia platform is not powered by any ML afaik because their system is very closed and not easily tuned.

Can you point us to one search engine you know and we know which uses semantic search?

Have you seen vespa.ai ?

No I haven't so thanks for the link. It looks very good which makes me cautious. Is it a pig with make-up, I wonder. What's your experience from using vespa.ai?

Extremely positive.

Very high performance, and much deeper support for tensors than elasticsearch has with with its new dense_vector type. Sadly they both have the same "gotcha" in that it is only really practical to run any _decent_ ranking model as a second phase re-rank to avoid linear scans.

I agree that FTS is not optimal, but it uses core CS tricks so I think it's worth learning. Word vectors are also intersting as a concept on their own.