How to work with embedding models and build a RAG application

In this episode, we’ll dive into embedding models—important tools for working with vector databases and large language models. These models convert text into numerical representations, making it easier to process and retrieve information efficiently. After covering the core concepts, we’ll apply them in practice by building a Retrieval-Augmented Generation (RAG) app using Small Language Models (SLMs) and a vector database. Learn more: https://aka.ms/Learn-Collection Pre-requisites: VSCode installed on your computer [eventID:25027]

How to work with embedding models and build a RAG application

Microsoft Reactor

How to work with embedding models and build a RAG application

Comments

Description

Microsoft Reactor

Transcript

A short introduction to SLMs, Reasoning models and local model with use cases

Knowledge Graph or Vector Database… Which is Better?

Chat with SQL and Tabular Databases using LLM Agents (DON'T USE RAG!)

"I want Llama3 to perform 10x with my private knowledge" - Local Agentic RAG w/ llama3

Uphold Trust in EU Data Protection: How We Navigate GDPR | Chetan Chugh | YeurDreamin

A Fun & Absurd Introduction to Vector Databases • Alexander Chatzizacharias • GOTO 2024

Trump holds DISASTER Event on Tariffs…BLOWS UP Economy

Prompt Engineering, RAG, and Fine-tuning: Benefits and When to Use

How to Build & Sell AI Agents: Ultimate Beginner’s Guide

The Man Who Almost Broke Math (And Himself...)

Model Context Protocol (MCP), clearly explained (why it matters)

RAG Fundamentals and Advanced Techniques – Full Course

Store All Data Types with Agentic RAG in n8n

Fine-Tuning Text Embeddings For Domain-specific Search (w/ Python)

Build a RAG Based LLM App in 20 Minutes! | Full Langflow Tutorial

Large Language Models (LLMs) - Everything You NEED To Know

Building Production RAG Over Complex Documents

Oren Cass - Understanding Trump Tariffs Through the Lens of “The New Conservatives” | The Daily Show

How to Create Custom GPT | OpenAI Tutorial

Python RAG Tutorial (with Local LLMs): AI For Your PDFs