How to Build a Production-Ready RAG AI Agent in Python

Ever dreamed of taking your fun AI hacks and turning them into rock-solid, production-ready apps? This step-by-step guide shows you how to glue together Inngest for event orchestration, Qdrant for your vector database, and LlamaIndex for handling data—then wrap it all up with observability, logging, retries, throttling, rate limits and concurrency controls so you’re ready for real-world traffic.

You’ll get a full breakdown of the architecture, code samples, and handy timestamps to jump between project demo, API setup, dev server, PDF chunking, querying your vector DB, front-end integration and more. Plus, if you want zero-fluff mentorship to actually ship AI projects and land a job, check out the DevLaunch program.

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