Vector Databases Cheat Sheet

Vector databases are specialized data storage systems optimized for high-dimensional vector embeddings, enabling fast similarity search at scale. Unlike traditional databases that retrieve exact matches through structured queries, vector databases find semantically similar items by computing distances between numerical representations of unstructured data like text, images, audio, and video. They power modern AI applications—from retrieval-augmented generation (RAG) systems that ground large language models in factual knowledge, to agentic AI with persistent memory, recommendation engines, semantic and multimodal search, and real-time anomaly detection. As the AI industry matures, vector databases are becoming load-bearing production infrastructure—with advances in quantization (RaBitQ achieving 32× compression at 95% recall), disk-based indexing (DiskANN), natively multimodal embeddings (Gemini Embedding 2, Jina v4), and advanced RAG architectures (GraphRAG, Agentic RAG) reshaping what these systems can achieve. The key insight: by representing complex data as points in a multi-dimensional space, vector databases make "meaning" mathematically searchable, transforming how applications discover and retrieve relevant information.