smlar
PostgreSQL extension for an effective similarity search || mirror of git://sigaev.ru/smlar.git || see https://www.pgcon.org/2012/schedule/track/Hacking/443.en.html (by jirutka)
magnitude
A fast, efficient universal vector embedding utility package. (by plasticityai)
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
smlar
Posts with mentions or reviews of smlar.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-04-22.
- Creating tfidf or bm25 indexes iin Postgres
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Pgvector – vector similarity search for Postgres
I have been using Smlar for a while for cosine similarity. Might this project provide a viable alternative? Smlar can be quite slow
https://github.com/jirutka/smlar
magnitude
Posts with mentions or reviews of magnitude.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-06-23.
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Text Classification Library for a Quick Baseline
(3) FastText now supports multiple languages [2].
[1] https://github.com/plasticityai/magnitude#pre-converted-magn...
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Pgvector – vector similarity search for Postgres
Check out Magnitude, we built it to solve that problem: https://github.com/plasticityai/magnitude
It's still loaded from a file, but heavily uses memory-mapping and caching to be speedy and not overload your RAM immediately. And in production scenarios, multiple worker processes can share that memory due to the memory mapping.
Disclaimer: I'm the author.
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Build an Embeddings index from a data source
General language models from pymagnitude
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Tutorial series on txtai
Backed by the pymagnitude library. Pre-trained word vectors can be installed from the referenced link.