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Comment by andrewlu0 | Hacker News Reader

Yea! Our FE is React/Next and BE is a mix of Supabase, Pinecone, BigQuery and QStash. and mix of HF endpoints and PoplarML in our batch for some embedding models

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thanks for using Supabase andrew.

Are you using it to store the embeddings (pgvector)? I'm asking to see where you're differentiating between supabase & pinecone. We get this question often and it's interesting to hear what others are doing and where they feel pgvector isn't appropriate

Hey! Loving the experience so far - we started the project before pgvector was supported on Supabase, and we want to support hybrid search as well (don't think pgvector supports this?)

> hybrid search

Yes, I believe it can be done but it requires a bit of work on your side. I'll see if we can come up with a demo and/or postgres extension which handles this for you

looping back here. The langchain community just released a version of Hybrid Search which using postgres full text search. It's clever, and IMO probably a better approach than just sparse/dense vectors:

https://js.langchain.com/docs/modules/indexes/retrievers/sup...

Will check this out - it seems like its doing two separate searches where you specify two separate top K values. Curious what the trade offs are between this approach and weighting sparse/dense vectors

Thanks guys for sharing it. This really helps us in the community to look for the architectures that we should focus on and the new interesting developments.

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