{
  "id": 17950283,
  "title": "Building a RAG System: Making AI Understand Your Documents",
  "url": "https://urgent.news/2026/11/03/building-a-rag-system-making-ai-understand-your-documents",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-03T10:58:46.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/abhi18/building-a-rag-system-making-ai-understand-your-documents-56c2"
  },
  "original_language": "en",
  "account": "Retrieval-Augmented Generation (RAG) is a method that enhances Large Language Models (LLMs) by allowing them to access and incorporate information from private documents. Traditional LLMs lack knowledge of the contents within these documents. RAG bridges this gap by retrieving relevant content from a set of documents and using it as context for AI-generated responses. This approach aims to make AI responses more pertinent to the specific documents being utilized, reducing the necessity for users to manually sift through large files.\n\nThe Cloud Notes RAG project provides a practical demonstration of RAG in action. Utilizing Python, PyPDF for PDF extraction, Google Gemini API for embeddings and AI responses, ChromaDB as a vector database, and Streamlit for an interactive user interface, the project showcases a comprehensive RAG system. The project's process commences with the extraction of text from five PDF documents, containing around 135 pages in total. This text is then segmented into smaller, overlapping chunks to ensure the preservation of contextual information.\n\nSubsequent to segmentation, the Gemini embedding model transforms these text chunks into numerical representations known as embeddings. These embeddings are subsequently stored in ChromaDB, which functions as the repository for storing document embeddings and facilitating their retrieval. Upon a user's query, the system first converts the question into an embedding and subsequently searches ChromaDB for the most relevant document sections. A maximum of six relevant chunks are retrieved and subsequently fed to the Gemini language model as context. The Gemini model then generates a response based on the retrieved information.\n\nRAG offers significant benefits for applications that require access to specific information, such as technical manuals, educational resources, corporate knowledge bases, and research papers. It enables users to retrieve pertinent information without the need to read through entire documents. However, RAG systems are not without their limitations. The quality of answers is contingent on the efficiency of document extraction, retrieval, and the context available. For instance, this particular implementation of RAG utilizes PyPDF for text extraction and lacks OCR functionality for image-only scanned documents, which could further enhance its capabilities.",
  "summary": "Large Language Models (LLMs) can generate impressive answers, but they have one important limitation: they may not know the information contained in your private documents. This is where Retrieval-Augmented Generation (RAG) becomes useful. RAG combines document retrieval with AI-generated responses. Instead of depending only on information learned during training, an AI application can retrieve…",
  "key_points": [
    "RAG enhances LLMs by accessing private document information",
    "Cloud Notes RAG project demonstrates RAG using Python, PyPDF, Gemini API, ChromaDB, and Streamlit"
  ],
  "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."
}