Storing word / document vectors in RDBMS

• qdrant

  139 17,839 9.9 Rust

  Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

  

A better option would be using a dedicated open-source vector database like Qdrant, it is more efficient, scalable, and has a convenient API. https://github.com/qdrant/qdrant (disclaimer: I'm part of the team).

• postgres-word2vec

  2 140 2.6 C

  utils to use word embedding models like word2vec vectors in a PostgreSQL database

I've recently stumbled upon smaller projects, like FREDDY (https://github.com/guenthermi/postgres-word2vec), a Postgres extension that looks interesting. The ability to write ad-hoc similarity queries in SQL seems like it might be valuable in some circumstances. I'm not sure about performance or storage efficacy.

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• tensorstore

  8 1,280 9.6 C++

  Library for reading and writing large multi-dimensional arrays.

  

There are tons of other ways to store vector data, one was just recently released - https://github.com/google/tensorstore

• txtai

  354 6,953 9.3 Python

  💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

  

I have project, txtai that supports vector/semantic/similarity search. It pairs an ANN index with a relational database to support SQL queries.

Suggest a related project