Building an End-to-End AI RAG Platform from Scratch

🚀 I Built a Production-Ready AI RAG Platform with React, FastAPI, LangChain & ChromaDB

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.

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.

✨ Key Features

• 📂 Upload PDF, DOCX, TXT, CSV, and Excel files
• 📑 Automatic document chunking
• 🧠 Embedding generation
• 🗄️ ChromaDB vector storage
• 🔍 Semantic similarity search
• 🤖 AI-powered document chat
• ⚙️ Custom pipeline builder
• 📊 Real-time dashboard
• 📄 Export chat history as PDF

🛠️ Tech Stack

Frontend

• React
• TypeScript
• Tailwind CSS

Backend

• FastAPI
• Python
• PostgreSQL

AI

• LangChain
• ChromaDB
• Sentence Transformers
• Llama 3

Deployment

• Docker
• Nginx

🔄 RAG Workflow

1. Upload Documents
2. Chunk Text
3. Generate Embeddings
4. Store in Vector Database
5. Semantic Search
6. Generate AI Response

The dashboard visualizes every step of the pipeline, making it easy to monitor document processing and AI interactions.

💡 What I Learned

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.

🚀 What's Next?

• Hybrid Search (Keyword + Vector)
• Streaming Responses
• Multi-tenant Workspaces
• Citation & Source References
• Support for Multiple Vector Databases
• AI Agent Workflows with LangGraph

I'd love to hear your thoughts! What features would you add to a production-ready RAG platform?