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.
Each symptom may require a different strategy: diagnose and address them independently. A single project can have multiple symptoms at once, and fixing one (e.g. adding hybrid search for keyword misses) may not fix the others (e.g. redundant results may still need MMR; poor precision may still need reranking).
| Symptom | Strategy |
|---|---|
| Missing exact/keyword matches | Hybrid search |
| Right documents exist but rank low (good recall, poor precision) | Multistage queries / reranking |
| Dense retriever misses relevant items entirely, or reranking too costly | Relevance feedback |
| Results are redundant / near-duplicate | MMR |
| Need to steer with example points | Recommendation / Discovery API |
| Need business-logic-based ranking | Score boosting |
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.