Chromadb

The previous post covered the architecture and indexing pipeline. 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. ...

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, the data and indexing pipeline, and the stack decisions behind the whole system. Later posts cover keyword vs semantic search, the AI assistant, and lessons learned. ...

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. ...