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Gen AI – RAG Application Development using LlamaIndex

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Udemy Coupon Code for Gen AI – RAG Application Development using LlamaIndex Course. Learn LlamaIndex to Develop RAG Applications using Open AI GPT, Gemini LLM and Vector Databases

Created by Manas Dasgupta | 7.5 hours on-demand video course

RAG Application Development Course Overview

Gen AI – RAG Application Development using LlamaIndex

This course uses Open AI GPT and Google Gemini APIs, LlamaIndex LLM Framework and Vector Databases like ChromaDB and Pinecone, and is intended to help you learn how to build LLM RAG applications through solid conceptual and hands-on sessions. This course covers all the basic aspects to learn LLM RAG apps and Frameworks like Agents, Tools, QueryPipelines, Retrievers, Query Engines in a crisp and clear manner. It also takes a dive into concepts of Language Embeddings and Vector Databases to help you develop efficient semantic search and semantic similarity based RAG Applications. We will also cover multiple Prompt Engineering techniques that will help make your RAG Applications more efficient.

List of Projects/Hands-on included:

  • Basic RAG: Chat with multiple PDF documents using VectorStore, Retriever, Nodepostprocessor, ResponseSynthesizer and Query Engine.
  • ReAct Agent: Create a Calculator using a ReAct Agent and Tools.
  • Document Agent with Dynamic Tools : Create multiple QueryEngineTools dynamically and Orchestrate queries through Agent.
  • Semantic Similarity: Try Semantic Similarity operations and get Similarity Score.
  • Sequential Query Pipeline: Create Simple Sequential Query Pipeline.
  • DAG Pipeline: Develop complex DAG Pipelines.
  • Dataframe Pipeline: Develop complex Dataframe Analysis Pipelines with Pandas Output Parser and Response Synthesizer.
  • Working with SQL Databases: Develop SQL Database ingestion bots using multiple approaches.

What you’ll learn

  • Fundamentals of LLM RAG Application Development
  • Using Open AI GPT API to develop RAG Applications
  • Prompt Engineering – Write Optimized Prompts for your RAG Application
  • Using LlamaIndex Query Engines, Retrievers and Query Pipelines
  • Building Conversational Memory
  • Using Data Connectors
  • Building Smart Agents and Tools
  • Language Embeddings and Vector Databases
  • Working with SQL Databases
  • Working with Structured Data and Dataframes in RAGs
  • Convert your LlamaIndex RAG as a FAST API

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Who this course is for

  • Software Developers aspiring to use the power of LLMs to build Gan AI RAG Applications as part of their Project and Products
  • Software Developers looking to automate their Software Engineering processes using Gen AI

Taught by Manas Dasgupta

Josh Smith
Josh Smith

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