The Excellence
Directory Platform Template

A search architecture that combines dense vector embeddings (semantic search) with sparse representations like BM25 (lexical search) to achieve better overall search quality. The industry standard approach for production RAG systems in 2026.

Advanced document chunking approaches that combine multiple chunking methods (fixed-size, semantic, structural) to optimize retrieval in RAG systems. Hybrid strategies adapt to document characteristics for superior performance.

Best practices for combining vector and keyword search using RRF and weighted fusion for improved retrieval accuracy in RAG systems.

A pagination technique for efficiently scrolling through large vector database result sets using cursors instead of offsets. Essential for retrieving all vectors in a collection or iterating through search results without performance degradation.

Complete guide to metadata filtering in vector search covering pre-filtering, post-filtering, and hybrid approaches. Addresses the Achilles heel of vector search with modern solutions.

Comprehensive guide to combining BM25 keyword search with vector semantic search using reciprocal rank fusion and reranking. Essential pattern for production RAG systems in 2026.

Best practices for backup and disaster recovery in vector databases. Covers full/incremental backups, replication strategies, and cloud-native approaches for safeguarding high-dimensional embeddings.

Comprehensive strategies for reducing vector database costs including storage management, compute optimization, and monitoring. Covers cloud pricing trends and hidden costs in 2026.