Build an AI Chatbot with One API Key: From Zero to Production in 30 Minutes

Build an AI Chatbot with One API Key: From Zero to Production in 30 Minutes This tutorial builds a production-ready AI chatbot backend with streaming responses, conversation history, model switching, and proper error handling. We'll use Python, FastAPI, and the OpenAI SDK pointed at an API aggregator so you can use any model. Prerequisites pip install fastapi uvicorn openai Step 1: Basic Chat Endpoint from fastapi import FastAPI from fastapi.responses import StreamingResponse from openai import OpenAI from pydantic import BaseModel app = FastAPI () client = OpenAI ( api_key = " sk-lemon-xxx " , base_url = " https://api.lemondata.cc/v1 " ) class ChatRequest ( BaseModel ): message : str model : str = " gpt-4.1-mini " conversation_id : str | None = None @app.post ( " /chat " ) async def chat ( req : ChatRequest ): response = client . chat . completions . create ( model = req . model , messages = [{ " role " : " user " , " content " : req . message }] ) return { " reply " : response . choi