How Vector Search Actually Works: IVF and HNSW
Every system that does "semantic" anything — RAG pipelines, recommendation engines, image search, dedup — boils down to one operation: given this vect…
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Vector databases explained
Behind almost every "AI-powered search" and RAG feature sits the same quiet workhorse: similarity search over vectors. If you're building anything tha…
Embeddings Magic
Transforming language into geometry. Introduction Embeddings are one of the most important building blocks of modern AI applications, yet they're ofte…
Build a RAG Pipeline From Scratch (Production Patterns That Actually Matter)
Most RAG tutorials stop at "embed your docs, do a similarity search, stuff the results in a prompt." That gets you a demo. It does not get you somethi…
RAG with OpenAI Embeddings, pgvector and LangChain
Retrieval-Augmented Generation (RAG) is a practical pattern: store knowledge as embeddings, retrieve the most relevant chunks with semantic search, th…