{
  "id": 13471472,
  "title": "Build a RAG pipeline from scratch — the actually simple version",
  "url": "https://urgent.news/2026/10/10/build-a-rag-pipeline-from-scratch-the-actually-simple-version",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-10T17:10:17.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/aiunplugged/build-a-rag-pipeline-from-scratch-the-actually-simple-version-gen"
  },
  "original_language": "en",
  "account": "The article explains how to build a Retrieval-Augmented Generation (RAG) pipeline using only three libraries in Python, without any frameworks, cloud accounts, or configuration files. Retrieval-Augmented Generation is a technique where an LLM answers a question using text pulled from a private document store at query time, rather than relying on the model's pre-existing knowledge. RAG is useful when source data changes frequently, is private, or is large, and requires citing specific passages. The article outlines four steps in a RAG system: chunking the source documents, embedding the chunks, storing the chunks and embeddings in a vector database, and finally retrieving and generating the answer. The article provides a step-by-step guide on setting up the required libraries, chunking the documents, embedding them, storing them in a Chroma vector database, and finally retrieving and generating the answer using the Claude API. The example uses a list of short product documentation snippets as the source material, and demonstrates how to chunk the text into smaller passages, embed them using the sentence-transformers library, and store them in a local Chroma vector database. Finally, the article shows how to retrieve the closest chunks to a user's query and generate an answer using the Claude API.",
  "summary": "Originally published on aiunplugged.in — cross-posting for the Dev.to community. Every RAG tutorial online starts with LangChain, LlamaIndex, or a hosted vector database signup. None of that is necessary to understand what a RAG pipeline actually is. This walkthrough builds one in a single Python file with three libraries — no framework, no cloud accounts, no config files. What RAG actually is…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}