Metadata Filtering

Combining vector similarity search with metadata filtering. Enables queries like find similar documents published after 2023 in category Technology.

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.

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 algorithm designed to make hybrid searches combining metadata filters and vector similarity more efficient, implemented in Apache Solr and other vector search systems.

k-Nearest Neighbors search finds the k closest vectors to a query vector in high-dimensional space. A fundamental operation in vector databases and machine learning, k-NN can be exact (brute force) or approximate (ANN) depending on performance requirements and dataset size.

A search approach that understands the meaning and intent of queries rather than just matching keywords. Using vector embeddings and similarity measures, semantic search finds conceptually relevant results even when exact terms don't match, enabling natural language queries and cross-lingual retrieval.