Managed vector databases are convenient, but building a search engine from scratch using a library like FAISS provides a deeper understanding of index types, latency tuning, and memory trade-offs, which is crucial for optimizing AI systems.
Systems like FAISS are optimized for vector similarity search and do not store the original data. Engineers must build and maintain a separate system to map the returned vector IDs back to the actual documents or metadata, a crucial step for production applications.
Retrieval Augmented Generation (RAG) uses vector search to find relevant documents based on a user's query. This factual context is then fed to a Large Language Model (LLM), forcing it to generate responses based on provided data, which significantly reduces the risk of "hallucinations."
For millions of vectors, exact search (like a FAISS flat index) is too slow. Production systems use Approximate Nearest Neighbor (ANN) algorithms which trade a small amount of accuracy for orders-of-magnitude faster search performance, making large-scale applications feasible.