03·2 notions
RAG
From naive RAG to production: embeddings, chunking, vector stores, hybrid search, reranking.
Apr 13,
Chunking
Split a text into blocks to embed them individually in a RAG. Size and overlap are critical parameters that impact precision, cost and latency.
Apr 13,
Vector Stores
Specialized database to store embeddings (vectors of numbers) and query them by similarity. Central building block of a RAG.