The latest articles on DEV Community by unistack solutions (@unistack_solutions). https://dev.to/unistack_solutions https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4007416%2F0148cced-fe3f-4734-8fa6-286185c1e78e.png https://dev.to/unistack_solutions en unistack solutions Mon, 29 Jun 2026 06:37:14 +0000 https://dev.to/unistack_solutions/building-an-end-to-end-ai-rag-platform-from-scratch-49f1 https://dev.to/unistack_solutions/building-an-end-to-end-ai-rag-platform-from-scratch-49f1 <h1> 🚀 I Built a Production-Ready AI RAG Platform with React, FastAPI, LangChain &amp; ChromaDB </h1> <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F63eviaa7jr0qmlpzk25r.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F63eviaa7jr0qmlpzk25r.png" alt=" " width="800" height="533"></a></p> <p>Over the past few weeks, I built a Retrieval-Augmented Generation (RAG) platform that enables users to create AI-powered knowledge bases and chat with their own documents.</p> <p>The goal was to create a flexible and production-ready system where developers and businesses can upload documents, build custom AI pipelines, and retrieve accurate answers using semantic search.</p> <h2> ✨ Key Features </h2> <ul> <li>📂 Upload PDF, DOCX, TXT, CSV, and Excel files</li> <li>📑 Automatic document chunking</li> <li>🧠 Embedding generation</li> <li>🗄️ ChromaDB vector storage</li> <li>🔍 Semantic similarity search</li> <li>🤖 AI-powered document chat</li> <li>⚙️ Custom pipeline builder</li> <li>📊 Real-time dashboard</li> <li>📄 Export chat history as PDF</li> </ul> <h2> 🛠️ Tech Stack </h2> <p><strong>Frontend</strong></p> <ul> <li>React</li> <li>TypeScript</li> <li>Tailwind CSS</li> </ul> <p><strong>Backend</strong></p> <ul> <li>FastAPI</li> <li>Python</li> <li>PostgreSQL</li> </ul> <p><strong>AI</strong></p> <ul> <li>LangChain</li> <li>ChromaDB</li> <li>Sentence Transformers</li> <li>Llama 3</li> </ul> <p><strong>Deployment</strong></p> <ul> <li>Docker</li> <li>Nginx</li> </ul> <h2> 🔄 RAG Workflow </h2> <ol> <li>Upload Documents</li> <li>Chunk Text</li> <li>Generate Embeddings</li> <li>Store in Vector Database</li> <li>Semantic Search</li> <li>Generate AI Response</li> </ol> <p>The dashboard visualizes every step of the pipeline, making it easy to monitor document processing and AI interactions.</p> <h2> 💡 What I Learned </h2> <p>Building a production-ready RAG application involved more than connecting an LLM. Some of the biggest challenges included optimizing chunk sizes, improving retrieval accuracy, managing vector storage efficiently, and designing a clean user experience for pipeline creation.</p> <h2> 🚀 What's Next? </h2> <ul> <li>Hybrid Search (Keyword + Vector)</li> <li>Streaming Responses</li> <li>Multi-tenant Workspaces</li> <li>Citation &amp; Source References</li> <li>Support for Multiple Vector Databases</li> <li>AI Agent Workflows with LangGraph</li> </ul> <p>I'd love to hear your thoughts! What features would you add to a production-ready RAG platform?</p> ai webdev react python