The Excellence
Directory Platform Template

Popular cross-encoder reranker models trained on MS MARCO dataset for semantic search, providing superior accuracy in re-ranking the top results from bi-encoder retrieval systems.

Search approach combining keyword-based (BM25) and semantic (vector) search for best of both worlds. Uses fusion techniques like RRF to merge results.

Search technique understanding meaning and context rather than exact keyword matching. Uses vector embeddings to find semantically similar content even with different wording.

Family of algorithms trading perfect accuracy for speed in high-dimensional similarity search. Enables sub-linear query time with 90%+ recall on billion-scale datasets.

Finding nearest vectors in high-dimensional space based on distance or similarity metrics. Core operation of vector databases enabling semantic search, recommendations, and RAG.

Foundational IR textbook that includes content on vector‑space models and retrieval, providing essential background for understanding vector search and hybrid retrieval in modern vector databases.