Llm

Before MCP existed, adding tools to an AI application meant writing the same glue code over and over. You had OpenAI’s function calling syntax. Anthropic had tool use with a slightly different schema. LangChain abstracted over both, but now you depended on LangChain’s versioning decisions. Every new model provider meant rewriting your tool definitions. Every new tool meant re-registering it across every AI integration you maintained.

Every vendor selling software right now claims their product is “agentic.” I’ve seen chatbots with a system prompt called an agent. I’ve seen a scheduled Python script described as autonomous AI. I’ve also shipped actual agents to production, at an insurance company, handling FNOL triage, policy lookup, and claims routing. The gap between what gets marketed as an agent and what you’d actually build in production is wide, and it’s expensive to get wrong.

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 space move fast, 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.

Business users kept asking my team for one-off data pulls: how many orders above $150 in December, which products are low on stock, that sort of thing. Every request meant a developer dropping their work to write a throwaway SQL query. So I built a REST API that does the translation instead. It feeds the database schema to an LLM, gets back a SQL query, runs it, and returns JSON. One codebase, four interchangeable providers (OpenAI, Azure OpenAI, Claude, and Gemini), switched by a single environment variable.

Introduction # I built this chat app to answer a narrow question: how little infrastructure do you actually need to stream tokens from Azure OpenAI into a browser? Less than most tutorials assume. There is no SignalR here and no WebSocket hub.