{
  "id": 9112451,
  "title": "Building a Simple RAG System with Python, ChromaDB and Gemini",
  "url": "https://urgent.news/2026/09/22/building-a-simple-rag-system-with-python-chromadb-and-gemini",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-22T08:28:58.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/sanjay_kj_00362a63febb086/building-a-simple-rag-system-with-python-chromadb-and-gemini-3lh9"
  },
  "original_language": "en",
  "account": "Retrieval-Augmented Generation (RAG) is a technique that enables large language models (LLMs) to answer questions using information from external documents, rather than relying solely on their pre-existing knowledge. In a recent project while interning at Valentius Kryptix, a lightweight RAG system was built to demonstrate the benefits of this approach. The system was designed to bridge the gap between vector databases, which excel at finding relevant information, and language models, which are skilled at generating coherent explanations.\n\nA key aspect of RAG is ensuring that the retrieval process is semantically relevant, meaning that it matches the underlying meaning of the query rather than just matching exact words. To achieve this, the system uses embeddings to convert both the document and the user's question into vectors that represent their semantic meaning. In this case, the all-MiniLM-L6-v2 model from Sentence Transformers was employed to generate these embeddings.\n\nThe ChromaDB vector database was then used to store these embeddings, allowing the system to quickly locate conceptually related content, even when the exact wording differs. To illustrate the effectiveness of this approach, the project compared the model's responses with and without retrieval. When asked \"What are the operations performed on a queue?\", without RAG, Gemini provided a general answer based on its training data. However, with RAG, the model generated a more accurate and detailed response, mentioning specific queue operations such as Enqueue, Dequeue, Peek/Front, Rear, isFull, and isEmpty.\n\nThe comparison between the RAG-enhanced and non-RAG responses highlighted the value of retrieval in providing contextually relevant information. Ultimately, the project emphasized that a RAG system is an essential combination of a vector database and a language model. While each component has its strengths and weaknesses individually, together they form a powerful tool for document-based question-answering. The full build process, along with code and implementation details, can be found in the original article.",
  "summary": "Large Language Models are impressively fluent, but they have a blind spot: they can only answer from what they learned during training. Ask one a question about a specific PDF, a company's internal handbook, or a niche textbook, and it may guess, generalize, or simply admit it doesn't know. Retrieval-Augmented Generation (RAG) closes this gap by giving an LLM a way to look things up before it…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Dev.to",
        "title": "Building a Serverless AI Tech-News Bot with Node.js, Gemini and GitHub Actions",
        "url": "https://urgent.news/2026/09/21/building-a-serverless-ai-tech-news-bot-with-node-js-gemini-and-github",
        "published": "2026-09-21T18:20:18.000Z"
      }
    ]
  },
  "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."
}