{"slug": "building-a-simple-rag-system-with-python-chromadb-and-gemini", "title": "Building a Simple RAG System with Python, ChromaDB and Gemini", "summary": "During an AI internship at Valentius Kryptix, a developer built a lightweight Retrieval-Augmented Generation (RAG) system using Python, ChromaDB, and Google's Gemini, tested against a Data Structures and Algorithms PDF. The system uses Sentence Transformers' all-MiniLM-L6-v2 embeddings to retrieve semantically relevant passages, and a side-by-side comparison showed that Gemini's answers were grounded in the source document only when retrieval was enabled. The developer framed RAG as a bridge between a vector database that finds information and a language model that explains it.", "body_md": "Large Language Models are impressively fluent, but they have a blind spot: they can only answer from what they learned during training.\n\nAsk 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.\n\nRetrieval-Augmented Generation (RAG) closes this gap by giving an LLM a way to look things up before it answers — turning a closed-book exam into an open-book one.\n\nDuring my AI internship at **Valentius Kryptix**, I built a lightweight RAG system to explore exactly this idea, using a Data Structures and Algorithms PDF as the test document.\n\nRather than treating RAG as a black box, the goal was to understand *why* each stage of the pipeline exists — not just how to wire it together.\n\n🔍 The core insight: retrieval only works if \"relevance\" is defined semantically, not literally.\n\nA keyword search for \"queue operations\" could fail if the document phrases the same concepts as \"Enqueue and Dequeue functions.\"\n\nThis is where embeddings come in. Using `all-MiniLM-L6-v2` via Sentence Transformers, the system converts both the document and the user's question into vectors that capture meaning rather than exact wording.\n\nStoring these vectors in **ChromaDB** makes it possible to find conceptually related content even when the exact words don't match.\n\nThe most instructive part of the project wasn't the pipeline itself, but comparing the model's behavior **with and without retrieval**.\n\nAsked:\n\n\"What are the operations performed on a queue?\"\n\nWithout RAG, Gemini answered from general training knowledge — correct in spirit, but disconnected from the actual source material.\n\nWith RAG, the same question returned an answer grounded in the specific document, correctly surfacing:\n\n• Enqueue\n\n• Dequeue\n\n• Peek/Front\n\n• Rear\n\n• `isFull`\n\n• `isEmpty`\n\nThat side-by-side comparison made the value of retrieval tangible in a way that reading about RAG never quite does.\n\n💡 **The bigger takeaway:** a RAG system is really a bridge between two very different technologies — a vector database good at *finding* information, and a language model good at *explaining* it.\n\nNeither one alone solves document-based Q&A well; together, they compensate for each other's weaknesses.\n\nThis piece is a shorter reflection on that design insight. The full build — including the complete five-step workflow, code, and implementation details — is covered in the original article.\n\n📖 Read the full article:\n\n[https://valentiuskryptix.com/how-to-build-a-retrieval-augmented-generation-rag-system-in-just-5-steps/](https://valentiuskryptix.com/how-to-build-a-retrieval-augmented-generation-rag-system-in-just-5-steps/)", "url": "https://wpnews.pro/news/building-a-simple-rag-system-with-python-chromadb-and-gemini", "canonical_source": "https://dev.to/sanjay_kj_00362a63febb086/building-a-simple-rag-system-with-python-chromadb-and-gemini-3lh9", "published_at": "2026-09-22 08:28:58+00:00", "updated_at": "2026-09-22 08:52:46.501941+00:00", "lang": "en", "topics": ["ai-tools", "large-language-models", "natural-language-processing", "ai-products", "developer-tools"], "entities": ["Valentius Kryptix", "ChromaDB", "Gemini", "Sentence Transformers", "all-MiniLM-L6-v2", "Python", "Google"], "alternates": {"html": "https://wpnews.pro/news/building-a-simple-rag-system-with-python-chromadb-and-gemini", "markdown": "https://wpnews.pro/news/building-a-simple-rag-system-with-python-chromadb-and-gemini.md", "text": "https://wpnews.pro/news/building-a-simple-rag-system-with-python-chromadb-and-gemini.txt", "jsonld": "https://wpnews.pro/news/building-a-simple-rag-system-with-python-chromadb-and-gemini.jsonld"}}