qdrant-hybrid-search-prefetches

Hybrid search in Qdrant combines sparse BM25 vectors with dense vectors by constructing prefetch queries. This approach enhances both keyword matching and semantic retrieval accuracy for your specific query types.

Using multiple vector types in Qdrant involves constructing effective prefetch queries for hybrid retrieval. You combine dense and sparse vector search across multiple fields to optimize both performance and accuracy.

Managing multi-field searches in Qdrant requires strategies for combining multiple representations when the same item has embeddings across various fields or languages. You configure multi-field retrieval within your hybrid search prefetch queries.

FastEmbed is required to implement sparse and dense vector search in Qdrant. It provides the embedding models, including sparse BM25 and dense models, needed to generate vectors for your hybrid search queries.

Choosing sparse vector models for text search in Qdrant depends on your use case context. Recommendations focus on selecting the best sparse models, like BM25, to optimize combined search performance alongside dense vectors.