ann-benchmarks VS clojure

On top of my head, pgvector only supports 2 indexes, those are running in memory only. They don't support GPU indexing, nor Disk based indexing, they also don't have separation of query and insertions.

Also with different people I've talked to, they struggle with scale past 100K-1M vector.

You can also have a look yourself from a performance perspective: https://ann-benchmarks.com/

https://ann-benchmarks.com/ is a good resource covering those libraries and much more.

We utilized the ANN Benchmarks methodology, a standard for benchmarking vector databases. Our tests used the dbpedia dataset of 1,000,000 OpenAI embeddings (1536 dimensions) and inner product distance metric for both Pinecone and pgvector.

Data warehouses are columnar stores. They are very different from row-oriented databases - like Postgres, MySQL. Operations on columns - e.g., aggregations (mean of a column) are very efficient.

Most vector databases use one of a few different vector indexing libraries - FAISS, hnswlib, and scann (google only) are popular. The newer vector dbs, like weaviate, have introduced their own indexes, but i haven't seen any performance difference -

Reference: https://ann-benchmarks.com/

(Blog author here). Thanks for the question. In this case the index for both DiskANN and pgvector HNSW is small enough to fit in memory on the machine (8GB RAM), so there's no need to touch the SSD. We plan to test on a config where the index size is larger than memory (we couldn't this time due to limitations in ANN benchmarks [0], the tool we use).

To your question about RAM usage, we provide a graph of index size. When enabling PQ, our new index is 10x smaller than pgvector HNSW. We don't have numbers for HNSWPQ in FAISS yet.

[0]: https://github.com/erikbern/ann-benchmarks/

In terms of "All You Need" for Vector Search, ANN Benchmarks (https://ann-benchmarks.com/) is a good site to review when deciding what you need. As with anything complex, there often isn't a universal solution.

txtai (https://github.com/neuml/txtai) can build indexes with Faiss, Hnswlib and Annoy. All 3 libraries have been around at least 4 years and are mature. txtai also supports storing metadata in SQLite, DuckDB and the next release will support any JSON-capable database supported by SQLAlchemy (Postgres, MariaDB/MySQL, etc).

pg_vector doesn't perform well compared to other methods, at least according to ANN-Benchmarks (https://ann-benchmarks.com/).

txtai is more than just a vector database. It also has a built-in graph component for topic modeling that utilizes the vector index to autogenerate relationships. It can store metadata in SQLite/DuckDB with support for other databases coming. It has support for running LLM prompts right with the data, similar to a stored procedure, through workflows. And it has built-in support for vectorizing data into vectors.

For vector databases that simply store vectors, I agree that it's nothing more than just a different index type.

The reason https://ann-benchmarks.com is so good, is that we can see a plot of recall vs latency. I can see you have some latency numbers in the leaderboard at the bottom, but it's very difficult to make a decision.

As a practitioner that works with vector databases every day, just latency is meaningless to me, because I need to know if it's fast AND accurate, and what the tradeoff is! You can't have it both ways. So it would be helpful if you showed plots showing this tradeoff, similar to ann-benchmarks.

5. Clojure - $96,381

I have a tangential question that is related to this cool new feature.

Warning: the question I ask comes from a part of my brain that is currently melted due to heavy thinking.

Context: I write a fair amount of Clojure, and in Lisps the code itself is a tree. Just like this F# parallel graph type-checker. In Lisps, one would use Macros to perform compile-time computation to accomplish something like this, I think.

More context: Idris2 allows for first class type-driven development, where the types are passed around and used to formally specify program behavior, even down to the value of a particular definition.

Given that this F# feature enables parallel analysis, wouldn't it make sense to do all of our development in a Lisp-like Trie structure where the types are simply part of the program itself, like in Idris2?

Also related, is this similar to how HVM works with their "Interaction nets"?

https://github.com/HigherOrderCO/HVM

https://www.idris-lang.org/

https://clojure.org/

I'm afraid I don't even understand what the difference between code, data, and types are anymore... it used to make sense, but these new languages have dissolved those boundaries in my mind, and I am not sure how to build it back up again.

As an analogy - my face hasn't changed all that much in a past few years, and I haven't changed my profile picture in those few years. Does it really mean that I'm unmaintained/dead?

> Where can I find latest documentation [...]?

The answer is still https://clojure.org/. And https://clojuredocs.org/ but it's community-maintained so might occasionally be missing some things right after they're released. E.g. as of this moment Clojure 1.11 is still not there since the maintainer of the website has some technical issues deploying the updated version of the website.

For me personally, the best API-level documentation is the source code.

> Where can I find [...] tools / libraries in a easy to use page or section?

There's no central repository of all the available things since they can be loaded from many places (Clojars, Maven Central, other Maven repositories, S3, Git, local files).

But there are community-maintained lists, like the one you've mentioned at https://www.clojure-toolbox.com (fully manual, AFAIK) or the one at https://phronmophobic.github.io/dewey/search.html (automated but only for GitHub). Perhaps there are others but I'm not familiar with them - most of the time, I myself don't find that much value in such services as I'm usually able to find things with a regular web search engine or ask the community when I need something in particular.

They are written in Java, and implement a bunch of interfaces, so the implementation looks complicated, but they are basically just classes with head and tail fields.

https://github.com/clojure/clojure/blob/master/src/jvm/cloju...

Like this.

As a Java/Scala user you should check out Clojure! It is highly recommended (https://clojure.org)

Lisp is not a programming language, but a family of languages ​​with many dialects. The most famous dialects include Common Lisp, Clojure, Scheme and Racket. So after deciding that I was going to learn Lisp, I had to choose one of its dialects.

I learned Clojure to implement a plugin for Metabase (the tool my former company used for creating business dashboards). I probably won’t ever use the language anymore in the future, but learning functional programming was fun and eye-opening.

I thought you might be trolling. But then when I looked at the Clojure repo on Github https://github.com/clojure/clojure the last commit was 2 months back. There is some merit in your arguments.