“Finding ‘Abbey Road’ given ‘beatles abbey rd’ search with Postgres”
...God help you if Brad Pitt and or the Jonas Brothers ever played a role with exactly that name-match. The web and search (and the culture?) have become super biased toward video especially commercial offerings, and the sorting ranked by popularity means pages and pages of virtually identical content about that which you are not interested in.
More of a perspective from just trying to index crap on my own machine vs building a SaaS
> The Dark Side of the Moon
> OK Computer
Those are my 3 personal records ever. I feel so average now...
For GIN for example, perfomance depends a lot on the size of the search input (the fewer characters, the more rows to compare) as well as the number of rows/size of the index.
It also mentions GiST (another type of index which isn't mentioned anywhere else in the article)..
We went with API embeddings for a similar use case. The cold-start latency of local models across multiple workers ate more money in compute than just paying per-token. Plus you avoid the operational overhead of model updates.
The hybrid approach in this article is smart. Fuzzy matching catches 80% of cases instantly, embeddings handle the rest. No need to run expensive vector search on every query.
My experience here is also related to music. Here are some cases to think about:
What's the actual title of the song "Mambo #5" vs. how you might search for it or find it referenced in other records? Mambo #5? Mambo No. 5? Mambo No. Five? Mambo Number 5? Mambo Number Five? And that's not even getting to the fact that the actual title is actually longer, with a parenthetical. This is a case where bigrams, trigrams, or other string similarly metrics wouldn't perform very well. Same with the Beatles song, is it "Dr. Robert" or "Doctor Robert"? Most string similarly algorithms put "Dr" and "Doctor" pretty far apart, but with vectors they should be practically equivalent.
How about "You've Lost that Loving Feeling"? Aren't there some dropped Gs in those gerunds? Is it You've Lost That Lovin' Feeling? You've Lost That Lovin' Feelin'? You've Lost That Loving Feelin'? In this case, string similarity (including trigrams) perform very well.
How about songs with censored titles? Some records will certainly have profanity censored, but would it be like "F*ck", "F**k", "F@$k", or what? And is the censorship actually part of the canonical song title, or just some references to it?
In the "#5" and "Dr." cases, this could be solved pretty effectively by the normalization step described in the article (hardcoding what #, No., and Dr. expand to) – although even that can get pretty complicated: what do you do about numbers? Do you normalize every numerical reference, e.g. "10 Thousand", to digits, or words? What about rarely used abbreviations, or cases where an abbreviation is ambiguous and could mean different things in different contexts? If someone has a song called "PT Cruiser" are you gonna accidentally normalize that to "Part Cruiser"? For this reason, I like to see this not as a "normalization" step, where there's a single normalized form, but rather a "query expansion" step – generate all the possible permutations, and those are your actual comparison strings.
It seems like embeddings could do the job of automatically considering different spellings/abbreviations of words as equivalent. I'm just a casual observer here, but I'm sure this is also a well-explored topic in speech-to-text, since you have to convert someone's utterances to match actual entity names, like movie titles for example.
lbrito•1w ago
alright2565•1w ago
Is the local model's quality sufficient for your use case, or do you need something higher quality?
storystarling•1w ago
With Ada your workers stay lightweight. For a bootstrapped project, I found it easier to pay the small API cost than to manage the infrastructure complexity of fat worker nodes.