cozo VS qdrant

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.

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.

I'm currently looking to implement locally, using QDrant [1] for instance.

I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].

[1]. https://qdrant.tech/

Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb

For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!

Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.

Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.

Qdrant serves as a vector database, optimized for handling high-dimensional data typically found in AI and ML applications. It's designed for efficient storage and retrieval of vectors, making it an ideal solution for managing the data produced and consumed by AI models like Mistral 7B. In our setup, Qdrant handles the storage of vectors generated by the language model, facilitating quick and accurate retrievals.