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.

The feedback model used during training and inference MUST be the same model. Formula weights during training are calibrated to that model's score distribution. If you switch feedback models, you must retrain.

What is a Good Feedback Model:

• If the model does not improve ranking quality when used as a reranker on retrieved documents, the RF search will not have a meaningful signal to amplify.
• RF search quality depends heavily on how well the feedback model scores partial matches. The training loss of RF formula relies on relative ordering, so poor score separation in the middle range (documents that are neither clearly relevant nor clearly irrelevant) weakens results.

To Make the RF API Work, You Need to Calibrate Weights First

Use when: setting up RF for a new use case — a new collection, feedback model, or embedding model powering ANN search.

RF uses a weighted formula that combines the original query vector with feedback signals.

For the currently available naive strategy, the learned weights control:

• a — how much to trust the original ANN query-document similarity
• b — how strongly differences in feedback scores matter
• c — how strongly to follow the feedback direction (toward relevant documents and away from irrelevant ones)

These weights must be learned from your data before use. You cannot safely use arbitrary values.

• Train the weights with the qdrant-relevance-feedback Python library, following the Training walkthrough. Use the same retriever and feedback model you will use at inference.