Postgresql

The infrastructure overhead in microservices projects is real. Setting up databases, wiring service discovery, getting distributed tracing working, managing connection strings across environments—this work hits before you’ve written a line of business logic. On more than one project, I’ve watched the first sprint disappear into Docker Compose files and appsettings drift.

If you’ve ever built a microservices architecture, you know the pain points all too well: spending hours setting up PostgreSQL locally, wrestling with Redis configurations, debugging why RabbitMQ won’t connect, managing connection strings across multiple services, and let’s not even talk about implementing distributed tracing manually.

TL;DR: This guide walks you through building a production-ready RAG system using FastAPI, ChromaDB, MinIO, and OpenAI. Learn document chunking, vector embeddings, hybrid search, and real-world deployment strategies. Introduction # As a .NET developer watching the AI landscape evolve, I found myself both excited and skeptical. When tools like Claude.ai and ChatGPT started offering out-of-the-box RAG solutions, I wanted to build my own system with full control over the implementation.