Range Search

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.

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.

The capability to filter vector search results based on metadata attributes before or during similarity search. Metadata filtering enables hybrid queries combining semantic search with structured constraints like dates, categories, tags, or user permissions, crucial for production RAG and search applications.

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.

Comparison of dense vector retrieval (neural embeddings) and sparse retrieval (keyword-based) approaches including strengths, weaknesses, and when to use hybrid methods.

Overview of distance metrics including Euclidean, cosine similarity, dot product, and Manhattan distance, with guidance on when to use each for optimal retrieval performance.