Vector Similarity Beyond Search(qdrant.tech)
qdrant.tech
Vector Similarity Beyond Search
https://qdrant.tech/articles/vector-similarity-beyond-search/
11 comments
I like their documentation in general and learned a lot from it. Especially since - unlike Pincore (which has good documentation too) - they don't focus primarily on their commercial offerings. I feel it's really written with the intention to inform first and to sell second.
All the diagrams are dead images for me :(
That's weird! A force refresh (CTRL+F5) doesn't work?
The shift to vector search approaches over exact text searches has, for me at least, made googling harder.
If I'm searching for something which has words which have a more common meaning than the context I care about - then exact matching ( of my carefully crafted search term ) performs much better than vector search.
Not every query is looking for the average result.
If I'm searching for something which has words which have a more common meaning than the context I care about - then exact matching ( of my carefully crafted search term ) performs much better than vector search.
Not every query is looking for the average result.
I would say that text text and vector search are orthogonal. Some scenarios are better with one, others with combination. But fitting vector search into the interface designed for text is limiting vector search potential
Interesting.
So how would you construct an interface specifically designed for a vector search?
So how would you construct an interface specifically designed for a vector search?
Vector search can be a useful tool for building a good search experience but people usually start at the wrong end of assuming they need it and then taking a few short cuts to just pick some technology and rush something out. This rarely leads to good results.
What I've seen:
- Vector search without good models does not tend to perform that well. I've seen comparisons where off the shelf free models struggle to keep up with simple text search and some manually tuned queries. Many companies might use those as a starting point but end up investing in their own models. BM25 (text ranking algorithm) provides a pretty solid baseline performance for a lot of things.
- Building good models is typically left as an exercise to the reader by those who provide vector search engines. These solutions are great for comparing vectors once you have them. However, getting good vectors is a bit of a dark art. And getting those is actually the hard part of the problem. Using a vector search engine is easy, getting good vectors isn't.
- Building good models to get good vectors requires a lot of expertise and skill. And not just technical skills. For example, understanding and building good processes for evaluating your search performance is not something they teach people in universities. I know some people and companies that can do this; they are not cheap (or bored). Cutting corners here leads to predictably meh results.
- The other thing you need is lots of data. The free open stuff that everybody else trains on as well is nice as a start but generally not good enough. That's why the likes of Google other big tech companies are so casual about sharing algorithms. They are worthless without data. And they're mostly not sharing data.
- Implementing vector search can be expensive. Basically it's function of hardware, people and time. It takes ages to train models, and it requires people who understand how to do that. You can speed it up with really expensive hardware. If your people make mistakes (because they don't know what they are doing), you'll burn a lot of time and hardware cost.
- Most startups or smaller companies don't really have the level of funding needed to do a proper job. Hence a lot of startups being a bit hand wavy about doing something something AI bla bla bla vector search on some beautiful slides. When you scrutinize these companies that usually means they have a (very) junior data "scientist" fresh out of college that heard a thing or two about how these things might actually work and not a whole lot else.
I've seem some companies doing this stuff properly. Some startups even. But not a lot. Sometimes you get the right mix of people and knowledge and ideas.
What I've seen:
- Vector search without good models does not tend to perform that well. I've seen comparisons where off the shelf free models struggle to keep up with simple text search and some manually tuned queries. Many companies might use those as a starting point but end up investing in their own models. BM25 (text ranking algorithm) provides a pretty solid baseline performance for a lot of things.
- Building good models is typically left as an exercise to the reader by those who provide vector search engines. These solutions are great for comparing vectors once you have them. However, getting good vectors is a bit of a dark art. And getting those is actually the hard part of the problem. Using a vector search engine is easy, getting good vectors isn't.
- Building good models to get good vectors requires a lot of expertise and skill. And not just technical skills. For example, understanding and building good processes for evaluating your search performance is not something they teach people in universities. I know some people and companies that can do this; they are not cheap (or bored). Cutting corners here leads to predictably meh results.
- The other thing you need is lots of data. The free open stuff that everybody else trains on as well is nice as a start but generally not good enough. That's why the likes of Google other big tech companies are so casual about sharing algorithms. They are worthless without data. And they're mostly not sharing data.
- Implementing vector search can be expensive. Basically it's function of hardware, people and time. It takes ages to train models, and it requires people who understand how to do that. You can speed it up with really expensive hardware. If your people make mistakes (because they don't know what they are doing), you'll burn a lot of time and hardware cost.
- Most startups or smaller companies don't really have the level of funding needed to do a proper job. Hence a lot of startups being a bit hand wavy about doing something something AI bla bla bla vector search on some beautiful slides. When you scrutinize these companies that usually means they have a (very) junior data "scientist" fresh out of college that heard a thing or two about how these things might actually work and not a whole lot else.
I've seem some companies doing this stuff properly. Some startups even. But not a lot. Sometimes you get the right mix of people and knowledge and ideas.
Isn't the problem more fundamental than that?
A vector embedding is choosing a single meaning for my search terms - and if that single meaning is wrong - because I'm not the average person - then I'll struggle to get relevant results.
I guess you can use context to do the mapping - but the rarer the thing I'm trying to find, the less likely this will work?
Note this happens both ends - in parsing both the query and parsing and indexing the original web page.
I suppose if it misinterprets query and page you might get a hit, but then the result you want might be page 700.
There is nothing more annoying than using a search term that you know should be pretty defining and finding the engine deciding to substitute it for a much more common search term.
It's a bit like the Google equivalent of MS clippy - you appear be searching for ......
A vector embedding is choosing a single meaning for my search terms - and if that single meaning is wrong - because I'm not the average person - then I'll struggle to get relevant results.
I guess you can use context to do the mapping - but the rarer the thing I'm trying to find, the less likely this will work?
Note this happens both ends - in parsing both the query and parsing and indexing the original web page.
I suppose if it misinterprets query and page you might get a hit, but then the result you want might be page 700.
There is nothing more annoying than using a search term that you know should be pretty defining and finding the engine deciding to substitute it for a much more common search term.
It's a bit like the Google equivalent of MS clippy - you appear be searching for ......
I think that's too simplistic. First, you can have more than one vector. More than one model even. The limitations here are cost, performance, memory and bandwidth. Second, search is of course subjective to some extent but that doesn't mean there's no right and wrong at all. A lot of that stuff boils down to context and having enough data in your context to come up with a good query.
And of course getting systematic about evaluating your search ranking performance. A lot of companies don't do that at all. A product manager checking things manually isn't quite good enough here. If you are just winging it like that, vector search isn't magically going to make it better.
And of course a text field is not a great source of any amount of meaning unless the user types a lot. Especially on mobile phones where typing is both tedious and error prone this is actually a huge problem. And the solution isn't better keyboards or training users to type better/more. If your users have to type a full sentence on a mobile phone before you return results, you've already failed them hard.
But what can you show them after they've typed merely two letters? Mostly vector search is more effective in recommendations or more like this style querying. Textual searches tend to be all over the place. The shorter the queries, the less useful vector search becomes. What's the meaning of "ba"? There isn't any other than that it's the beginning of something that may or may not start with those letters or have some of those letters in them and maybe even in that order.
The classic view of search is a text field and a list of a results is not the best mental model for this. Mostly Social Networks, Google (and others) try to pre-empt you having to search for anything by just magically showing you what you didn't know you were looking for. And when you search for stuff, they show you that other stuff anyway. Using Tik Tok is basically using search without using a text field. It just feeds on your behavior. Your behavior is all it needs to know as a context. It's not wrong as long as you keep on looking at stuff. The more you use it, the smarter it gets.
And of course getting systematic about evaluating your search ranking performance. A lot of companies don't do that at all. A product manager checking things manually isn't quite good enough here. If you are just winging it like that, vector search isn't magically going to make it better.
And of course a text field is not a great source of any amount of meaning unless the user types a lot. Especially on mobile phones where typing is both tedious and error prone this is actually a huge problem. And the solution isn't better keyboards or training users to type better/more. If your users have to type a full sentence on a mobile phone before you return results, you've already failed them hard.
But what can you show them after they've typed merely two letters? Mostly vector search is more effective in recommendations or more like this style querying. Textual searches tend to be all over the place. The shorter the queries, the less useful vector search becomes. What's the meaning of "ba"? There isn't any other than that it's the beginning of something that may or may not start with those letters or have some of those letters in them and maybe even in that order.
The classic view of search is a text field and a list of a results is not the best mental model for this. Mostly Social Networks, Google (and others) try to pre-empt you having to search for anything by just magically showing you what you didn't know you were looking for. And when you search for stuff, they show you that other stuff anyway. Using Tik Tok is basically using search without using a text field. It just feeds on your behavior. Your behavior is all it needs to know as a context. It's not wrong as long as you keep on looking at stuff. The more you use it, the smarter it gets.
I don't care about phone users not being able to type - I'm not searching from a phone.
In a way, that's exactly what I'm talking about - while search might be generally getting better for the average person, it's getting worse for me.
And sure that might be an economic inevitability - though I do wonder about the long tail of search.
We have 3 trends adding together:
* dumbing down the UI - removal of advances ways to tweak the search.
* vectorisation misinterpreting my search.
* ever improving SEO manipulation.
All these mean I see a significant drop in relevance when I'm searching for non-standard stuff - typically of a science/technical nature.
> The more you use it, the smarter it gets.
So if I'm looking for something new, it sends me the same old stuff? Great.
Not saying the companies aren't doing stuff that makes sense for them - it's just not working for me.
In a way, that's exactly what I'm talking about - while search might be generally getting better for the average person, it's getting worse for me.
And sure that might be an economic inevitability - though I do wonder about the long tail of search.
We have 3 trends adding together:
* dumbing down the UI - removal of advances ways to tweak the search.
* vectorisation misinterpreting my search.
* ever improving SEO manipulation.
All these mean I see a significant drop in relevance when I'm searching for non-standard stuff - typically of a science/technical nature.
> The more you use it, the smarter it gets.
So if I'm looking for something new, it sends me the same old stuff? Great.
Not saying the companies aren't doing stuff that makes sense for them - it's just not working for me.
Makes one wonder what other use cases are lurking that would need just another small modification and haven't even been thought of yet because they used to be impossible to implement.