RAG with MongoDB
Retrieval-augmented generation needs somewhere to store and search the embeddings it retrieves context from — and increasingly, teams are choosing to keep that vector data in the same database already holding their application's operational data, rather than standing up a...
Vector Search Performance Optimization
A vector search system that performs beautifully in a demo with a thousand test vectors can slow to a crawl once it's handling millions of embeddings and real production traffic. Getting vector search to perform well at scale isn't one fix — it's a set of levers spanning...
Exact Search vs Approximate Search
Every vector search system faces the same fundamental choice: guarantee the mathematically correct answer, or accept a very good answer in exchange for much greater speed. This is the exact-versus-approximate search trade-off, and picking the right side of it — often for...
ANN Search Explained
When you search a database of a million vector embeddings for the ones closest to your query, checking every single one is often too slow to be practical at scale. Approximate Nearest Neighbor (ANN) search is the technique that makes vector search fast enough for real...