sqlite-ecosystem VS sqlite-vss

I definitely plan to! I have a much larger list of SQLite extensions I've built here: https://github.com/asg017/sqlite-ecosystem

Here's a few other references you may enjoy if you wanna learn more about SQLite extensions:

- The single source file for sqlite-vec: https://github.com/asg017/sqlite-vec/blob/main/sqlite-vec.c

- sqlean, a project from Anton Zhiyanov which is good base of great SQLite extensions: https://github.com/nalgeon/sqlean

- The official SQLite docs: https://www.sqlite.org/loadext.html

- The "hello world" SQLite extension example: https://www.sqlite.org/src/file/ext/misc/rot13.c

Author here, happy to answer any questions! My "Introducing sqlite-vss: A SQLite Extension for Vector Search" [0] blog post has more details about the motivations behind this extensions, along with a demo. Includes a semantic search engine hosted on Datasette, build with sqlite-vss and sentence-transformers, on fly.io. Simple and cheap!

Though my favorite part about this extension (and all my other SQLite extensions[1]): you can `pip install sqlite-vss` for Python, `npm install sqlite-vss` for Node.js, or use https://deno.land/x/sqlite_vss for Deno! The underlying SQLite extension has been compiled on different platforms and uploaded to pip/npm/deno.land/x to make distribution easier. There's also pre-compiled extensions on each Github release if you just want to use it with the sqlite3 CLI.

Currently working on getting Mac M1 arm builds, hopefully by end of this week [2]

[0] https://observablehq.com/@asg017/introducing-sqlite-vss

[1] https://github.com/asg017/sqlite-ecosystem

[2] https://github.com/asg017/sqlite-vss/issues/13

I guess this is an answer to the GitHub issue I opened against SQLite-vss a couple of months ago?

https://github.com/asg017/sqlite-vss/issues/124

Perhaps sqlite-vss? It adds vector searches to sqlite.

https://github.com/asg017/sqlite-vss

Utilizing sqlite-vss to store and query vector embeddings managed by a local SQLite database, Semantify conducts fast, precise vector searches within these embeddings to find and recommend relevant content, ensuring readers are presented with articles that truly match their interests.

Whether you’re navigating through well-known options like SQLite, enriched with the sqlite-vss extension, or exploring other avenues like Chroma, an open-source vector database, selecting the right tool is paramount. This article compares these two choices, guiding you through the pros and cons of each, helping you choose the right tool for storing and querying vector embeddings for your project.

Here is a SQLite extension that uses Faiss under the hood.

https://github.com/asg017/sqlite-vss

Not associated with the project, just love SQLite and find it very useful.

Vector Databases: As your data grows, efficiently searching through millions of vectors can become a challenge. Specialized vector databases like FAISS, Annoy, or Elasticsearch's vector search capabilities can be explored to manage and search through large-scale vector data. Your sentence is grammatically correct. In addition, databases like SQLite and PostgreSQL have extensions, such as sqlite-vss and pgvector, that can be used to store and query vector embeddings, respectively.

I had a go at one of those a few months ago: https://datasette.io/plugins/datasette-faiss

Alex Garcia built a better one here as a SQLite Rust extension: https://github.com/asg017/sqlite-vss

Since the problem domain is that of anomaly detection from constructed request feature embeddings, I wonder if an ANN-search methodology using an embedded database (such as https://github.com/asg017/sqlite-vss or similar) was explored.

As I searched for "sqlite vector plugin" I didn't find any results, before a couple of weeks ago. Two weeks ago I found Alex' SQLite VSS plugin for SQLite. The library was an amazing piece of engineering from an "idea perspective". However, as I started playing around with it, I realised it was ipso facto like "Titanic". Beautiful and amazing, but destined to leak water and sink to the bottom of the ocean because of what we software engineers refers to as "memory leaks".