RAG AI Agent Application with PydanticAI: Step by Step
#aiagents #aiagent #aiapplications
🤖 AI Agent Application with PydanticAI: Step by Step
In this video we dive into the world of Retrieval-Augmented Generation (RAG) AI agents? In this step-by-step tutorial, I'll show you how to build a powerful RAG AI agent using PydanticAI, PostgreSQL, and OpenAI. 🚀
In this video, you will learn:
📂 How to create a vector database from embedded data extracted from PDF documents.
🤖 How to leverage PydanticAI for querying this vectorized data effectively.
🔗 How to integrate a PostgreSQL database
🧠 How to utilize OpenAI embeddings via PydanticAI AI Agent Framework
By the end of this tutorial, you’ll have a fully functional AI agent capable of querying and retrieving insights from your PDF-based data seamlessly. Whether you're an AI enthusiast, a developer, or a data scientist, this video has everything you need to get started.
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🔗 Useful Links:
Github with notes and code
https://github.com/skolo-online/pydan...
PydanticAI RAG Documentation
https://ai.pydantic.dev/examples/rag/
Get Digital Ocean Account with Credit
https://m.do.co/c/7d9a2c75356d
Buy me a Beer
https://www.buymeacoffee.com/feyitaje
Medium Article on the Video
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⏰ Timestamps:
00:02:17 Getting started with the tutorial
00:06:04 Get PostgreSQL database
00:09:33 Install all the pre-requisites
00:11:39 Start with database code - embed data that will be used for the RAG Agent with PydanticAI
00:21:38 Create a storage bucket for hosting your PDF documents
00:31:07 Get in to the PydanticAI RAG Agent code which includes agent tools and functions
00:48:13 Test embedding code for the AI Agent on the command line
00:54:38 Test the AI Agent run function from command line
01:00:00 Install a streamlit application to use as front-end to upload documents to the RAG Agent and ask questions
01:06:09 Demonstrate the completed PydanticAI RAG Agent
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