cozo VS pgvector

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.

# Install Postgres and pgvector !apt-get update && apt install postgresql postgresql-server-dev-14 !git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git !cd pgvector && make && make install # Start database !service postgresql start !sudo -u postgres psql -U postgres -c "ALTER USER postgres PASSWORD 'pass';"

When talking about Vector Databases, in the market we can find the specialized ones and multi-model, most of the major database providers like Oracle, PostgreSQL or MongoDB, for mention some of them, have integrated a specific solution to retrieve vector data.

For this example, I wanted to focus on how pgvector  – an open-source vector similarity search for Postgres – can be used to identify data similarities that exist in enterprise data.

pgvector supports dense vector search well, but it does not have plan to support sparse vector.

There's an issue in the pgvector repo about someone having several ~10-20million row tables and getting acceptable performance with the right hardware and some performance tuning: https://github.com/pgvector/pgvector/issues/455

I'm in the early stages of evaluating pgvector myself. but having used pinecone I currently am liking pgvector better because of it being open source. The indexing algorithm is clear, one can understand and modify the parameters. Furthermore the database is postgresql, not a proprietary document store. When the other data in the problem is stored relationally, it is very convenient to have the vectors stored like this as well. And postgresql has good observability and metrics. I think when it comes to flexibility for specialized applications, pgvector seems like the clear winner. But I can definitely see pinecone's appeal if vector search is not a core component of the problem/business, as it is very easy to use and scales very easily

The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. On this example we use features like: Embedding, Prompts, ETL and save all embedding on PGvector(Postgres Vector database)

Official GitHub repository of the pgvector extension

pgvector 0.6.0 was just released and will be available on Supabase projects soon. Again, a special shout out to Andrew Kane and everyone else who worked on parallel index builds.

The pgvector extension adds vector similarity search capabilities to your PostgreSQL database. To use the extension, you have to first create it in your database. You can install the extension, by connecting to your database and running the CREATE EXTENSION command from the psql command prompt: