Module 6

Searching From Examples and Feedback

Some searches have no query text. A reader clicks “more like this”, or an analyst has three documents that are right and two that are wrong and no words for what separates them.

The Recommendation API searches from positive and negative examples. The Discovery API takes context pairs, where each pair names one region of the vector space to move toward and one to move away from.

When a better model or a click log disagrees with your ranking, relevance feedback folds that disagreement into the next query, across the whole collection. It needs a second model and three weights fitted to your own setup, so start with the tutorial below.

A point passed in by ID, whether as example, positive, negative, or a relevance feedback target, is left out of the results. Pass its raw vector instead to keep it eligible.

• Recommendation API and Discovery API: searching from examples and from context pairs.
• Relevance Feedback: the query interface and what the naive strategy computes.
• Relevance Feedback Retrieval in Qdrant: fitting the weights and evaluating the result.