Why You Need a Vector Database
Understand the limitations of brute-force similarity search, how approximate nearest neighbor algorithms like HNSW work, and what problems vector databases solve in production.
The Limits of In-Memory Search
NumPy semantic search works well for small corpora, but it has a fundamental scalability problem: every search scans every vector. At 1 million documents, each query requires 1.5 billion floating-point multiplications, taking hundreds of milliseconds. Worse, all vectors must fit in RAM.
Production AI systems need search over millions of documents in under 50ms. This is what vector databases are designed to deliver.
Approximate Nearest Neighbor Search
Approximate Nearest Neighbor (ANN) algorithms trade a small amount of accuracy for dramatic speed improvements. Instead of checking every vector, ANN algorithms use smart indexing structures to skip large portions of the search space.
In practice, ANN returns the true nearest neighbor over 95% of the time while being 100-1000x faster than exact search. For RAG, this tradeoff is almost always worth it.
All lessons in this course
- Why You Need a Vector Database
- Getting Started with Pinecone
- pgvector: Embeddings in PostgreSQL
- Choosing and Benchmarking Vector Stores