Vector DB
Vector databases are essential for enabling semantic search and Retrieval-Augmented Generation (RAG) in AI. We benchmarked leading platforms to inform the technical selection of the optimal retrieval layer for LLMs.
Vector Database Benchmark: 7 Open-Source Engines for RAG
Vector DBBenchmarkSep 4We benchmarked seven open-source, self-hosted vector databases as the retrieval layer of a RAG pipeline, each run one at a time on identical bge-m3 embeddings and real medical and technical queries, so the database index was the sole variable. The workload spanned MedRAG-50k, TechQA-28k, and a 2.25M-vector corpus across eight dimensions, from accuracy and retrieval…
- Vector DBBenchmarkJul 20
Vector Database Sizing and Selection Calculator
The practical question behind a self-hosted vector database for RAG is which engine fits a given server, and which one the workload rules out. The calculator below answers both, from our benchmark of seven self-hosted vector databases run at matched recall on identical embeddings. Five checkboxes at the top of the calculator name five common…