How to Build a Production-Ready RAG AI Agent in Python (Step-by-Step)

Video Description

Get started with Inngest: https://innge.st/yt-twt-1 👉 Check out PyCharm, the only Python IDE you need to build data models and AI agents. Download now. Free forever, plus one month of Pro included: https://jb.gg/PyCharm-for-Tim I'll show you how to build an AI RAG application in Python and how to get it ready to deploy to production. I myself have made many AI projects on this channel, you've probably seen a few of them. And while those projects are super fun and cool and you can learn a lot, they're not ready to be deployed into the wild and used in a production environment. That's because they're missing observability, logging, retries, throttling rate, limiting all of the things that you need for a real production grade AI app. DevLaunch is my mentorship program where I personally help developers go beyond tutorials, build real-world projects, and actually land jobs. No fluff. Just real accountability, proven strategies, and hands-on guidance. Learn more here - https://training.devlaunch.us/tim?video=AUQJ9eeP-Ls 🎞 Video Resources 🎞 Inngest Python Docs: https://www.inngest.com/docs/apps Qdrant: https://qdrant.tech/ LlamaIndex: https://www.llamaindex.ai/ Code in this video: https://github.com/techwithtim/ProductionGradeRAGPythonApp ⏳ Timestamps ⏳ 00:00:00 | Overview 00:01:21 | Project Demo 00:04:07 | Architecture & Tools Breakdown 00:08:23 | Project Setup & Dependencies 00:11:22 | API Setup 00:12:10 | Inngest Dev Server Setup 00:25:06 | Vector Database Setup 00:36:48 | Loading & Chunking PDFs 00:58:09 | Querying Our VectorDB 01:08:54 | Adding the Frontend 01:13:56 | Rate Limiting, Throttling & Concurrency Hashtags #RAGCoding #AIAgent #Python