I Built a RAG Pipeline in TypeScript Without LangChain — The Whole Thing in 200 Lines

Every RAG tutorial I found looked like this: const chain = RetrievalQAChain . fromLLM ( model , vectorStore . asRetriever ()); const res = await chain . call ({ query : " what is this document about? " }); Twelve lines, a Pinecone key, a screenshot of it answering one question about one PDF, and a confident closing paragraph about "production readiness." I read four of them and still couldn't have told you what an embedding actually was, why cosine similarity was the metric everyone used, or what would happen if my documents were 800 pages instead of 8. I could copy the code. I couldn't debug it. So I deleted the frameworks and wrote the whole thing by hand. No LangChain, no LlamaIndex, no hosted vector database, and no cloud LLM — the model runs on my laptop. Six files, a bit over 200 lines of TypeScript, and nothing imported that I can't explain. This post is the whole pipeline, the data structures behind each stage and why they were chosen, the four bugs that cost me the most time, and a debugging method that will save you an afternoon. Who this is for I'm assuming you write JavaScript or TypeScript, you're comfortable with async / await , arrays, and classes, and you've installed an npm package before. That's it. I am not assuming you know anything about machine learning, vectors, embeddings, or information retrieval. Every one of those is explained from zero as it comes up, and if a line of code does something non-obvious, I explain the line. If you already know what a vector store is, skip to the bug list at the bottom. What RAG actually is Strip the acronym away and RAG is one idea: Language models can't read your files. So find the relevant paragraphs yourself, paste them into the prompt, and ask the question. The rest of the pipeline exists to make that sentence practical. Finding the right paragraphs is the hard part. You can't keyword-search your way there, because a user asking "how do I stop duplicate rows" won't use the word "DISTINCT" that appears in