qdrant-search-strategies

What problem does it solve?

This guide helps developers improve search results when using Qdrant by selecting and applying advanced strategies such as hybrid search, reranking, and relevance feedback.

Core Features & Use Cases

• Hybrid search: combine dense and sparse retrieval to improve recall across datasets.
• Reranking and multistage queries: refine top results with cross-encoder or ColBERT style rerankers for higher precision.
• Relevance Feedback: leverage user feedback signals to steer future retrieval across the vector space.
• Diversity and ranking controls: use methods like MMR and score boosting to improve result variety.
• Use Case: optimize product catalogs or knowledge bases where exact keyword matches and semantic similarity both matter.

Quick Start

Select a suitable strategy for your dataset and apply it to your Qdrant retriever to improve results.