qdrant-search-strategies

# How to Improve Search Results with Advanced Strategies These strategies complement basic vector search. Use them after confirming the embedding model is fitting the task and HNSW config is correct. If exact search returns bad results, verify the selection of the embedding model (retriever) first. If the user wants to use a weaker embedding model because it is small, fast, and cheap, use reranking or relevance feedback to improve search quality. ## Missing Keyword Matches or Need to Combine Multiple Search Signals Use when: pure vector search misses keyword/domain term matches, or the use case benefits from combining searches on multiple representations (including languages and modalities) of the same item. See how to use [hybrid search](https://skills.qdrant.tech/qdrant-search-quality/search-strategies/hybrid-search/SKILL.md) ## Right Documents Found But Not in the Top Results Use when: good recall but poor precision (right docs in top-100, not top-10). - See how to use [Multistage queries](https://skills.qdrant.tech/md/documentation/search/hybrid-queries/?s=multi-stage-queries), for example with late interaction rerankers through [Multivectors](https://skills.qdrant.tech/md/documentation/manage-data/vectors/?s=multivectors). - Cross-encoder rerankers via FastEmbed [Rerankers](https://skills.qdrant.tech/md/documentation/fastembed/fastembed-rerankers/) ## Dense Retriever Misses Relevant Items or Reranking Is Too Costly Use when: dense retriever misses relevant items...