A simple search engine from scratch

https://bernsteinbear.com/blog/simple-search/

by bertman • 1 year ago

296 59 1 year ago

franczesko3• 1 year ago

On the topic of search engines, I really liked classes by David Evans. The task was also building a simple search engine from scratch. It's really for beginners, as the emphasis is on coding in general, but I've found it to be very approachable.

https://www.cs.virginia.edu/~evans/courses/

marginalia_nu• 1 year ago

The SeIRP-book, free online as a PDF, is also a fantastic resource on traditional search engines and information retrieval in general.

[1] https://ciir.cs.umass.edu/irbook/

franczesko3• 1 year ago

Due to dead links, this is more appropriate url:

https://www.cs.virginia.edu/~evans/courses/cs101/

StefanBatory2• 1 year ago

Server not found. Did HN gave it hug of death?

fuzztester• 1 year ago

the actual course link on udacity gives a 404.

ktallett3• 1 year ago

I always wonder if the days of search engines for specific topics could return. With LLM's providing less than accurate results in some areas, and Google, bing, etc being taken over by adverts or well organised SEO, there feels like a place for accurate, specialised search.

datadrivenangel• 1 year ago

The curation of an index of resources is what's needed for niche search

dcist• 1 year ago

WestLaw and Lexis Nexis provide this for legal search, but quite frankly, these services are subpar. It's amazing that these two companies rake in hundreds of millions but they are both slower than Google, Bing, Yandex, or any LLM service (ChatGPT, Claude, Gemini, etc.) while scouring a universe of text that is orders of magnitude smaller. The user experience is also terrible (you have to login and specify a client each and every time you attempt to use the service and both services log you out after a short -- in my opinion -- period of inactivity, creating friction and needless annoyance to the user). There's an opportunity there.

raydenvm• 1 year ago

Which is not scalable, right?

cosmicgadget4• 1 year ago

My hope is that content self-indexes so instead curation it just has to be aggregated.

cyanydeez2• 1 year ago

i know the answer is never distributed services, but if one could build a sufficiently complex SDK to make like a Blue Sky but for niche search indexes, you could chain a bunch of vetted resources together.

fanwood• 1 year ago

I already directly search on Wikipedia for most topics (with a search shortcut on URL bar)

ktallett3• 1 year ago

Wikipedia is useful up to a point for sure. I feel whether it could be a expansion of Wikipedia in it's current use case, but for emerging research and niche topics it can sometimes be less useful.

wolfgang422• 1 year ago

Yeah, the (relative) rise of Kagi and Marginalia show that from a technical perspective, this is within the grasp of a dedicated hobbyist.[1] If Google continues their current trajectory, and overwhelming numbers of AI crawlers don’t cause an unsurmountable rise in CAPTCHA pages, I hope to see an upsurgence of niche search engines that focus on some specialty small enough that one or a few people can curate the content and produce a much better experience than the current crop of general Web search engines.

Self-plug: I run such a search engine (for programmers) in my living room, at <https://search.feep.dev/>. I don’t spend a ton of time maintaining it, so I’m interested to see what someone really dedicated could do.

[1] I wrote a 2004-vs-2014 comparison, and things have only gotten better since then: https://search.feep.dev/blog/post/2022-07-23-write-your-own

iLoveOncall• 1 year ago

Please, Kagi doesn't even have 50,000 active members, it's definitely not "rising" to become a serious contender at any sort of market share, it's a micro-project. You just feel it's bigger than that because for some reason all of its 50,000 users post relentlessly about it on HN.

cyanydeez2• 1 year ago

Just gotta build a search engine that properly contextualizes scams, bait & switch sites, SEO, and the rest, and you're back in business.

To do that, you probably still need humans to properly curate the dataset, essentially hire 100 librarians and setup a work flow for them to continually prune results.

Right now, everything is all batch processes. None of these LLMs use active feedback since there's no real models using updates.

sp0rk• 1 year ago

The SVG equation is very difficult to read if you're using a dark OS theme because the blog uses the OS preference for dark/light theme (and doesn't seem to give an option to change it manually, either.)

tekknolagi2• 1 year ago

Fixed, I think? Let me know

DylanSp• 1 year ago

Works now (I noticed the same issue).

dheera• 1 year ago

On the side, not criticizing OP but I hate the word "cosine similarity" and I wish people would just call it a "normalized dot product" because anyone who took sophomore-level university calculus would get it, but instead we all invented another word

cosmicgadget4• 1 year ago

This was a really nice read. Now I have no excuse not to upgrade my blog search. I do feel that I'll have a ton of long tail words like 'prank'.

snowstormsun2• 1 year ago

Nice idea, but this approach does not handle out of vocabulary words well which is one major motivation for using a vector-based search. It might not perform significantly better compared to lexical matching like tf-idf or BM25, and being slower because of linear complexity. But cool regardless.

netdevphoenix• 1 year ago

It is supposed to be a simple search engine. Keyword: simple.

As long as it does what it is meant to, as a simple search engine, it seems fine

snowstormsun2• 1 year ago

Using tfidf or bm25 would actually be simpler than a vector search.

I understand this is just for fun, just wanted to point that out.

haasisnoah• 1 year ago

How would you handle those in wordvec?

And isn’t a big advantage that synonyms are handled correctly. This implementation still has that advantage.

cosmicgadget4• 1 year ago

Or since OP has both the cosine similarity matching and naive matching, a heuristic combination of the two since they address each other's weaknesses.

janalsncm2• 1 year ago

Vector based approaches either don’t handle OOV terms at all or will perform poorly, depending on implementation. If you limit to alphanumeric trigrams for example you can technically cover all terms but badly depending on training data.

swyx• 1 year ago

this embeds words with word2vec, which is like 10 years old. at least use BERT or sentencetransformers :)

gthompson512• 1 year ago

I have been thinking a bit lately about how much sense that makes compared to just using word vectors, since traditional queries are super short and often keyword based(like searching for "ground beef" when wanting "ground beef recipes I can cook easily tonight") and so lack most of the context that BERT or similar gives you. I know there are methods like using seperate embeddings for queries and such, but maybe a basic word based search could be more useful, especially with something like fastText for out of vocabulary terms.

kaycebasques2• 1 year ago

The idea behind the search engine is to embed each of my posts into this domain by adding up the embeddings for the words in the post.

Ah, OK! I never really grokked how to use word-level embeddings. Makes more sense now.

skarz3• 1 year ago

Is 'grokked' a common verb now? I had never even heard the word until Musk's AI.

StefanBatory2• 1 year ago

It was a word before, as far as I remember. Saw it a few times here.

kaycebasques2• 1 year ago

A common verb "now"??

Grok (/ˈɡrɒk/) is a neologism coined by the American writer Robert A. Heinlein for his 1961 science fiction novel Stranger in a Strange Land. While the Oxford English Dictionary summarizes the meaning of grok as "to understand intuitively or by empathy, to establish rapport with" and "to empathize or communicate sympathetically (with); also, to experience enjoyment",[1] Heinlein's concept is far more nuanced, with critic Istvan Csicsery-Ronay Jr. observing that "the book's major theme can be seen as an extended definition of the term."[2] The concept of grok garnered significant critical scrutiny in the years after the book's initial publication. The term and aspects of the underlying concept have become part of communities such as computer science.

https://en.wikipedia.org/wiki/Grok

kevinsync• 1 year ago

I never knew the etymology [0] but always knew the word for as long as I've been into computing (90's) .. apparently it's from the 1960's from a Heinlein novel!

[0] https://en.wikipedia.org/wiki/Grok

BalinKing• 1 year ago

I first learned of from the Jargon File, long before Grok the product existed: https://www.catb.org/jargon/html/G/grok.html

janalsncm2• 1 year ago

Started hearing about it in ~2022 when some ML researchers accidentally left a model training on over a weekend. For a while the model wasn’t doing much (so they were going to turn it off) and then over the weekend it got surprisingly good.

https://en.m.wikipedia.org/wiki/Grokking_(machine_learning)

vojtechrichter• 1 year ago

I really like people playing around with technology many take for granted, without understanding its core, underlying princliples

leumassuehtam• 1 year ago

The author has a nice series on compiling a Lisp [0], but unfortunately his search engine fails to find it by querying it with "lisp" or "Lisp".

[0] https://bernsteinbear.com/blog/compiling-a-lisp-0/

tekknolagi2• 1 year ago

I wonder if that's just not in the top 10k words :/

freilanzer• 1 year ago

And it's unfinished since 2020.

A simple search engine from scratch

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