Qdrant Relevance Feedback

作者 qdrant6a03d0ce8f55無授權條款收錄於 2026年10月8日更新於 2026年10月8日

Expanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is too weak. Use when someone asks about 'Qdrant's Relevance Feedback API', 'improving dense search relevance/recall', 'how to discover/get more relevant results from vector search', 'cheaper/better alternative to reranking', 'using a more heavy/big embedding model for dense search but can't afford it', 'finding more relevant documents beyond the initial search pool', or 'feedback loops'. Also trigger when the user has a search quality problem due to a dense retriever being weak and is considering reranking as a solution — this API may be a better fit

僅公開檔案列表。將技能安裝到工作區後即可檢視檔案內容。

路徑大小類型
SKILL.md10.5 KBtext/markdown

來源與署名

來源:qdrant/skills位於skills/qdrant-search-quality/search-strategies/relevance-feedback提交6a03d0c

授權條款: 無授權條款

內容歸原作者所有。SourceWeft 從公開儲存庫中收錄這些內容。

檢舉或申請下架