Vector Databases

Vector databases are specialized database systems designed to store, index, and query high-dimensional vector embeddings. They're the foundation of modern AI applications, enabling semantic search, RAG systems, recommendation engines, and more.

What You'll Learn

In this topic cluster, you'll discover:

• Fundamentals: Understanding vector embeddings, similarity metrics, and indexing algorithms
• Getting Started: Setting up your first vector database and running queries
• Production Deployment: Scaling, performance optimization, and best practices
• Integration: Connecting vector databases with AI models and applications
• Advanced Topics: Hybrid search, multi-modal embeddings, and distributed systems

Why Vector Databases Matter

Traditional databases excel at exact matches, but modern AI applications need semantic understanding. Vector databases bridge this gap by:

• Finding similar items based on meaning, not just keywords
• Enabling AI applications to access relevant context (RAG)
• Powering recommendation systems with deep learning
• Supporting multi-modal search (text, images, audio)
• Scaling to billions of vectors with millisecond latency

Popular Vector Databases

• Qdrant: High-performance vector search engine
• Pinecone: Managed vector database service
• Weaviate: Open-source vector database with GraphQL
• Milvus: Distributed vector database for massive scale
• Chroma: Lightweight embedding database for LLMs

Start with the fundamentals below and work your way through building production systems.