I am looking into hybrid search for personalizing the results from my keyword based search. I want keyword search to define the candidate set and item popularity to define the main sort order. But I want use vector similarity to boost items similar to an embedding specified in the query. How can I control the weights/balance between the keyword based search result (and item popularity sorting) and the contribution from the vector search part?

👋 This is possible in the upcoming 0.25 release. We have release candidate build available, if you want to test.

Thank you @Kishore Nallan Regarding the gist on hybrid search: https://gist.github.com/jasonbosco/f4187f6b4f585d2dc8902af85408994a When reading the gist it looks like we have to provide a semantic model and let Typesense generate embeddings for each item using this model. We are not aiming for a hybrid of keyword and /semantic search/. We have our own local model for generating embeddings. We would like to enrich our index with one embeddings for each item. And then manually provide another embedding at query-time. We then want the hybrid search result to be a mix of the keyword search and vector search (based on similarity on embeddings). Is this possible? And can we control the weights between the keyword- and the vector-search? Or is it fixed to 0.7 vs 0.3?