The Excellence
Directory Platform Template

Comprehensive overview of vector database evolution in 2026, including the shift to vectors as data types, PostgreSQL dominance, 400% adoption surge, and $10.6B projected market by 2032.

DeepLearning.AI course teaching six practical vector database applications using Pinecone, including RAG for LLMs, recommender systems, and hybrid search combining images and text.

MongoDB Vector Search turns MongoDB into a full-featured vector database, enabling approximate and exact nearest neighbor search over vector embeddings stored alongside operational data. It supports semantic similarity search, retrieval-augmented generation (RAG) for AI applications, and lets you combine vector search with full‑text search and structured filters in the same query. Available on supported MongoDB Atlas clusters, it integrates with popular AI frameworks and services for building intelligent, agentic systems.

A collaboratively maintained Google Sheets matrix comparing features, capabilities, and characteristics of many vector databases and approximate nearest neighbor libraries, useful for selecting solutions for AI and similarity search applications.

A comprehensive 2023 survey that systematically analyzes the design, architecture, indexing techniques, and system implementations of modern vector database management systems, serving as a foundational reference for understanding the vector database ecosystem used in AI applications.