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. It is available as of Qdrant 1.17.

Read this whole skill before implementing: pick the usage pattern that matches the user's goal, and check the What NOT to Do list.

How It Works

A relevance feedback query takes the query (target), a few seed documents (feedback) scored by a feedback model (3-5 seeds is enough), and formula weights for the naive strategy. See Relevance Feedback for the request format.

The formula weights MUST be trained once per general search use case (your dataset, dense retriever, and feedback model). If you do not train them, results will at best be random and 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.

Feedback Model

A feedback model is any model that can produce a float relevance score for (query, document) pairs. Higher scores must always mean higher relevance.

Examples: a cross-encoder, embedding similarity (for example, cosine similarity between query and document embeddings, or max_sim for late interaction models), an LLM-based scorer, a custom ranker.