Openai

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.

macOS has had built-in dictation since Monterey. It is fine: press and hold a key, speak, done. But it requires Apple’s servers (unless you download the enhanced on-device model), only works in some apps, and you have zero control over punctuation, formatting, or hotkeys.

At some point I had three side projects each talking to a different LLM provider, with API keys pasted into three .env files and three slightly different client wrappers. Swapping GPT-4o for Claude in any of them meant editing code. That is a silly amount of friction for what is, underneath, the same chat-completion call, so I put a LiteLLM proxy in front of everything and never went back.

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.