How to Build a Production-Ready RAG AI Agent in Python (Step-by-Step)
Get started with Inngest: https://innge.st/yt-twt-1I'll show you how to build an AI rig 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 Receive SMS online on sms24.me
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