qdrant-relevance-feedback
Reranking reorders documents that have already been retrieved. Qdrant's Relevance Feedback (RF) instead modifies the vector search process itself based on a small amount of reranker feedback, distilling reranker (feedback model) knowledge into the search step. This allows RF to surface documents that the initial ANN search did not score highly enough.
The RF is intended for tasks where relevance correlates with similarity in vector space.
How you apply the RF depends on your goals.
First, understand how the RF works, read the ENTIRE section. Then define your goals and choose the appropriate usage pattern described below. Make sure to avoid the listed anti-patterns ("DO NOTs"). Before implementing anything, read CAREFULLY to avoid missing important details.
How It Works
The Qdrant Query Point API with a type RelevanceFeedbackQuery takes:
- a query (
target) - a small list of seed documents (
feedback) with relevance scores (often 4–5 seeds are enough) - formula weights, which MUST be trained once per general search use case (your dataset, dense retriever, and feedback model)
If you do not train the formula weights, results will at best be random, will not align with your data distribution or model behavior. Training is lightweight because the formula itself is simple.
During search, it scores each candidate by combining similarity to the original query, similarity to highly rated seed documents and dissimilarity to poorly rated ones.