Gen Ai - Llm Rag Two In One - Langchain + Llamaindex

Gen AI - Learn to develop RAG Applications using LangChain an LlamaIndex Frameworks using LLMs and Vector Databases

What you'll learn

Be able to develop your own RAG Applications using either LangChain or LlamaIndex

Be able to use Vector Databases effectively within your RAG Applications

Craft Effective Prompts for your RAG Application

Create Agents and Tools as parts of your RAG Applications

Create RAG Conversational Bots

Perform Tracing for your RAG Applications using LangGraph

Requirements

Python Programming Knowledge

Description

This course leverages the power of both LangChain and LlamaIndex frameworks, along with OpenAI GPT and Google Gemini APIs, and Vector Databases like ChromaDB and Pinecone. It is designed to provide you with a comprehensive understanding of building advanced LLM RAG applications through in-depth conceptual learning and hands-on sessions. The course covers essential aspects of LLM RAG apps, exploring components from both frameworks such as Agents, Tools, Chains, Memory, QueryPipelines, Retrievers, and Query Engines in a clear and concise manner. You'll also delve into Language Embeddings and Vector Databases, enabling you to develop efficient semantic search and similarity-based RAG applications. Additionally, the course covers various Prompt Engineering techniques to enhance the efficiency of your RAG applications.List of Projects/Hands-on included: Develop a Conversational Memory Chatbot using downloaded web data and Vector DBCreate a CV Upload and Semantic CV Search App Invoice Extraction RAG AppCreate a Structured Data Analytics App that uses Natural Language Queries ReAct Agent: Create a Calculator App using a ReAct Agent and ToolsDocument Agent with Dynamic Tools: Create multiple QueryEngineTools dynamically and orchestrate queries through AgentsSequential Query Pipeline: Create Simple Sequential Query PipelinesDAG Pipeline: Develop complex DAG PipelinesDataframe Pipeline: Develop complex Dataframe Analysis Pipelines with Pandas Output Parser and Response SynthesizerWorking with SQL Databases: Develop SQL Database ingestion BotThis twin-framework approach will provide you with a broader perspective on RAG development, allowing you to leverage the strengths of both LangChain and LlamaIndex in your projects.

Overview

Section 1: Introduction

Lecture 1 Introduction to the Course

Lecture 2 Introduction to Large Language Models (LLMs)

Lecture 3 Introduction to Prompt Engineering

Lecture 4 Prompts Advanced

Section 2: Starting with LangChain

Lecture 5 Introduction to LangChain

Lecture 6 LangChain Environment Setup

Lecture 7 Installing Dependencies

Lecture 8 Using Google Gemini LLM

Lecture 9 Our First LangChain Program

Section 3: Learn LangChain through Projects

Lecture 10 Working with SQL Data - RAG Application

Lecture 11 Create a CV Upload and Search Application

Lecture 12 Create an Invoice Extract RAG Application

Lecture 13 Create a Conversational Chatbot for HR Policy Queries

Lecture 14 Analysis of Structured Data using Natural Language

Section 4: Getting Started with LlamaIndex

Lecture 15 Introduction to LlamaIndex

Lecture 16 LlamaIndex setup

Lecture 17 Our First LlamaIndex Program

Section 5: Learn LlamaIndex through Projects

Lecture 18 RAG App using Chroma DB Vector Database

Lecture 19 LlamaIndex RAG with SQL Database

Lecture 20 LlamaIndex Query Pipelines

Lecture 21 LlamaIndex Sequential Query Pipeline

Lecture 22 LlamaIndex Complex DAG Pipeline

Lecture 23 Setting up a DataFrame Pipeline

Lecture 24 Working with Agents and Tools

Lecture 25 Create a Calculator RAG App using ReAct Agents

Lecture 26 Create a Document Agent with Dynamically built Tools

Lecture 27 Create a Code Checker RAG App

Software Developers, Data Scientists, ML Engineers, DevOps Engineers, Support Engineers, Test / QA Engineers