cozo VS ann-benchmarks

I can figure parts of it out from https://github.com/cozodb/cozo/blob/main/cozo-core/tests/air_routes.rs which is enough to get started

I've been keeping an eye on https://github.com/cozodb/cozo which is pretty close to something I've wanted, a sqlite version of datalog/datomic.

Finally, the one product I was extremely impressed with and felt was genuinely impressive as a database in general was cozodb.

Take a look at cozodb. It meets most of your goals and I've been really enjoying using it. It might give you some inspiration or something to contribute to.

Sure. They're called 'partials' sometimes. Useful if you want to rerender just part of a page. This is a pattern used by HTMX, a 'js framework' that accepts fragments of html in an http response and injects it into the page. This is good because it avoids the flash and state loss of a whole page reload. See the HTMX essay on template fragments for a more complete argument [0].

This is a go template for an interactive todos app [1] that I'm experimenting with. The html content of the entire page is present in one template definition which is split into 6 inline {{block}} definitions / "fragments". The page supports 5 interactions indicated by {{define}} definitions, each of which reuse various block fragments relevant to that interaction. I'm in the process of converting it to use embedded cozodb [2] queries which act as a server side data store. The idea here is that the entire 'app', including all html fragments, styles, http requests and responses, db schema, and queries are embedded into this single 100-line file.

[0]: https://htmx.org/essays/template-fragments/

[1]: https://github.com/infogulch/go-htmx/blob/master/templates/t...

[2]: https://github.com/cozodb/cozo

If anyone wants to try a FOSS vector-relational-graph hybrid database for more complicated workloads than simple vector search, here it is: https://github.com/cozodb/cozo/

About the integrated vector search: https://docs.cozodb.org/en/latest/releases/v0.6.html

It also does duplicate detection (Minhash-LSH) and full-text search within the query language itself: https://docs.cozodb.org/en/latest/releases/v0.7.html

HN discussion a few days ago: https://news.ycombinator.com/item?id=35641164

Disclaimer: I wrote it.

You are probably talking about this cozo.

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.