pgvector-python

If you're using cosine similarity when retrieving for a RAG application, a good approach is to then use a "semantic re-ranker" or "L2 re-ranking model" to re-rank the results to better match the user query. There's an example in the pgvector-python that uses a cross-encoder model for re-ranking: https://github.com/pgvector/pgvector-python/blob/master/exam... You can even use a language model for re-ranking, though it may not be as good as a model trained specifically for re-ranking purposes. In our Azure RAG approaches, we use the AI Search semantic ranker, which uses the same model that Bing uses for re-ranking search results.

Looks like that there is recent work to make a pgvector example for this https://github.com/pgvector/pgvector-python/blob/master/exam...

Re 1) pgvector has an example in the repo that uses a model for re-ranking: https://github.com/pgvector/pgvector-python/blob/master/exam... I'm not using that in my own experiments since I don't want to worry about the performance of running a model on production, but seems worth a try.

There's also pgvector (trivial) examples for different languages, see e.g. github.com/pgvector/pgvector-python