Chromadb

The previous posts covered architecture and data ingestion. This one is about the core value proposition: why semantic search matters and how to demonstrate it. The approach: build both keyword and AI search, run the same queries through each, and document where keyword search fails. The results make the case for semantic search more effectively than any architectural explanation could. What keyword search actually does Postgres full-text search works by tokenizing text into lexemes (normalized words), removing stop words, and matching query tokens against indexed documents. It’s fast, deterministic, and has been reliable for decades. ...

The first post covered architecture. Here the focus shifts to data: how to generate a realistic product catalog at scale, why description quality matters for RAG, and how the ingestion pipeline embeds everything into ChromaDB. The pipeline produced 1180 products with rich descriptions, embedded them in 39 seconds, and returned retrieval results that actually held up. The archetype strategy Writing 1180 product descriptions by hand is infeasible. Having Claude write them one-by-one is slow and produces inconsistent output. The solution: archetype-based generation. ...

I built Ozark Ridge, a mock outdoor gear retail site with AI-powered product search and a Rufus-style product assistant. The project exists to demonstrate RAG (Retrieval-Augmented Generation) in a realistic e-commerce context. This is the first post in a series documenting the build. This one covers the architecture and stack decisions. Later posts cover the RAG pipeline, keyword vs semantic search comparison, and building the AI assistant. What it does Two features: ...

This series covers building a RAG pipeline to answer questions about the Anthropic documentation. A RAG agent answers questions by first searching a private knowledge base, then passing the relevant excerpts to an LLM as context — the model reads the actual source material before it responds, rather than guessing from training data. Here the focus is the retrieval layer: how to chunk text, embed it, retrieve it, and measure whether retrieval is actually working. ...