I chunked all the documents into 400-800 character chunks, vectorized them all, and put them in a vector database.
The results are pretty bad--the surfaced document chunks kind-of-but-not-really match up with the query.
I'm getting much better results from simple keyword searches (using meilisearch).
Am I doing something wrong? Do I need to use a fine-tuned model like BERT? Is this technology vastly overhyped?
My take is you might like keyword searches better for some queries and you might like embedding search for others.
The problems of: (1) How to combine keyword search and embedding search (you'd imagine you'd want a ranking function that handles both) and (2) How to handle chunks are both hard.
As for (2) you probably want to make the chunks as big as you practically can, you should be chunking on tokens instead of characters if you at all can.
With the chunks of course you don't get a score for the query-document relationship you get the query-chunk score instead which isn't quite the score you really want, aggregating all the chunk hits and properly chunking the data is an open problem to say the least.
This video suggests Elasticsearch is (or will be) able to blend the two: https://www.youtube.com/watch?v=5Qaxz2e2dVg
I'm hoping meilisearch adds that because ES is a beast of a software package.
Almost nobody in the commercial search field is doing quantitative evaluation despite
About a decade ago I worked on a neural-net powered search engine for patents that used a neural network to compress bag of words features into a 50-dimensional vector (think LDA on steroids) and used a patented algorithm to
https://patents.google.com/patent/CA2829569C
combine that result with the keyword vector. It kicked ass. When we put up a demo we got a call the first day from the USPTO wanting to buy it.
The thing is that algorithm searches over the feature vector and the residual of that feature vector in the bag-of-words space. There's no danger of infringing that with a BERT-like model because there isn't any such residual.
We tuned up w/ Gov2 data from TREC and that was essential for getting our parameters right.
If I were trying that now I would use logistic regression to make a probability estimator that "this document is relevant" that uses multiple scores as input; I'm not so sure how great results you will get but it is a rational basis to get started. TREC specifically does not reward training a probability estimator because they historically have been interested in "long tail" results and they don't think a kid sticking up his hand really high because he is really certain it adds value, but there are a lot of things with IR that are really hard (alerting) because we're mostly not using probability estimators and the ones we do make aren't that great (e.g. I never see p>0.7 for a conventional search engine tuned up that way.)
* Is that the right chunk size? How much of a chunk might contain the relevant information? Is it better for your use case to chunk by sentence? I've done RAG with document chunks, sentences, and triplets (source -> relation -> target). How you chunk can have a big impact.
* One approach that I've seen work very well is (1) first, use keyword or entity search to limit results, then (2) use semantic similarity to the query to rank those results. This is how, for example, they do it at LitSense for sentences from scientific papers: https://www.ncbi.nlm.nih.gov/research/litsense/. Paper here: https://academic.oup.com/nar/article/47/W1/W594/5479473.
* You still need metadata. For example, if a user asks for something like "show me new information about X," the concept of "new" won't get embedded in the text. You'll need to convert that to some kind of date search. This is where doing RAG with something like OpenAI function calls can be great. It can see "new" and use that to pass a date to a date filter.
* I've found some embeddings can be frustrating because they conflate things that can even be opposites. For example, "increase" and "decrease" might show up as similar because they both get mapped into the space for "direction." This probably isn't an issue with better (I assume higher dimensional) embeddings, but it can be problematic with some embeddings.
* You might need specialized domain embeddings for a very specific domain. For example, law, finance, biology, and so forth. Certain words or concepts that are very specific to a domain might not be properly captured in a general embedding space. A "knockout" means something very different in sports, when talking about an attractive person, or in biology when it refers to genetic manipulation.
