Results for “breadth-first-search”

Guides selection of Qdrant search strategies including hybrid search, reranking, relevance feedback, MMR, and discovery APIs to improve retrieval quality.

Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.

Enables fast similarity search and clustering of dense vectors using FAISS, supporting billions of vectors, GPU acceleration, and various index types.

Augments nearest-neighbour search with graph Laplacian features to retrieve items based on both semantic similarity and structural role.