Vector Databases 101

Curious about what vector databases are and how they power modern search and AI applications? Our “Vector Databases 101” blog series is the perfect place to start.

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• What Are Embeddings? How Semantic Search Works (and Where it Fails)

  Learn what embeddings are, how they’re created, and why they’re foundational to semantic search, recommendation, and modern AI. This post covers the basics of vector representations, how similarity is measured, and the real-world challenges of embedding-based search.

• Modern Search 101: Lexical, Semantic, and Hybrid Search Explained

  Explore the three pillars of modern search: lexical, semantic, and hybrid. This article explains how each approach works, their strengths and limitations, and how combining them leads to more relevant, robust, and intelligent search systems.

These articles cover the fundamentals of embeddings, semantic search, and the evolution of search technology. We recommend reading them for a strong foundation in vector search and retrieval-augmented generation (RAG).

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For more hands-on guides, check out the other topics in our Guides section.