Hello Guys, I have an hybrid Qdrant database (i.e sparse and dense vectors). When querying, I notice that the scores are very weak, even if the answer is correct. it rarely exceeds 0.5. Do you have any idea how can I properly set up the query_engine parameters ? (similarity_top_k, alpha,...) ?
imo scores are relative to the model, and not absolute measurements
openai embeddings for example, the similarities are usually between 0.7 and 0.9 for any query. For other embeddin models and methods, it can be different
