Building Agentic RAG From Scratch in Pure Python
Key Takeaways
Builds an agentic RAG pipeline from scratch using pure Python
Original Description
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🔗 GitHub Repository
https://github.com/daveebbelaar/ai-cookbook/tree/main/knowledge/agentic-rag
⏱️ Timestamps
00:00 Introduction to Agentic RAG
00:48 Semantic vs. Agentic RAG
02:12 Tool Definitions and Setup
04:11 Listing Files
06:01 Searching for Patterns
10:19 Reading Files
12:37 Building the Agent
13:31 Debugging the Agent
18:34 Structured Output
20:33 Production Considerations
25:19 Conclusion and Next Steps
📌 Description
Learn how to build an agentic RAG system from scratch in pure Python without relying on heavy frameworks. This tutorial walks through creating custom tools for listing, searching, and reading markdown files, then connecting them to an AI agent using Pydantic AI for iterative tool calling and self-correction. Whether you're moving past basic semantic RAG or integrating private company data into LLMs, this hands-on guide covers the full workflow from core code to production deployment.
👋🏻 About Me
Hi! I'm Dave, AI Engineer and founder of Datalumina®. On this channel, I share practical tutorials that teach developers how to build production-ready AI systems that actually work in the real world. Beyond these tutorials, I also help people start successful freelancing careers. Check out the links above to learn more!
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Chapters (11)
Introduction to Agentic RAG
0:48
Semantic vs. Agentic RAG
2:12
Tool Definitions and Setup
4:11
Listing Files
6:01
Searching for Patterns
10:19
Reading Files
12:37
Building the Agent
13:31
Debugging the Agent
18:34
Structured Output
20:33
Production Considerations
25:19
Conclusion and Next Steps
🎓
Tutor Explanation
DeepCamp AI