Develop powerful RAG Applications using Open AI GPT APIs, LangChain LLM Framework and Vector Databases

What you'll learn

Fundamental of LLM Application Development

LLM Frameworks with LangChain

Using Open AI GPT API to develop RAG Applications

Engineering Optimized Prompts for your RAG Application

LangChain Loaders and Splitters

Using Chains and LCEL (LangChain Expression Language)

Using Retreivers, Agents and Tools

Conversational Memory

Multiple RAG Projects with various Source Types and Business Use

Requirements

Basic Python Language

No Data Science experience needed

Description

This course on developing RAG Applications using Open AI GPT APIs, LangChain LLM Framework and Vector Databases is intended to enable learners who want to build a solid conceptual and hand-on proficiency to be able to solve any RAG automation projects given to them. This course covers all the basics aspects of LLM and Frameworks like Agents, Tools, Chains, Retrievers, Output Parsers, Loaders and Splitters and so on in a very thorough manner with enough hands-on coding. It also takes a deep dive into concepts of Language Embeddings and Vector Databases to help you develop efficient semantic search and semantic similarity based RAG Applications.List of Projects Included:SQL RAG: Convert Natural Language to SQL Statements and apply on your MySQL Database to extract desired Results.CV Analysis: Load a CV document and extract JSON based key information from the document.Conversational HR Chatbot: Create a comprehensive HR Chatbot that is able to respond with answers from a HR Policy and Procedure database loaded into a Vector DB, and retain conversational memory like ChatGPT.Structured Data Analysis: Load structured data into a Pandas Dataframe and use a Few-Shot ReAct Agent to perform complex analytics.For each project, you will learn:- The Business Problem- What LLM and LangChain Components are used- Analyze outcomes- What are other similar use cases you can solve with a similar approach.

Overview

Section 1: Introduction

Lecture 1 Introduction to Large Language Models

Lecture 2 Introduction to LangChain Framework

Lecture 3 Introduction to Prompts

Lecture 4 Code Demo - Simple ways of forming a Prompt and using it to Chain with a Model

Section 2: LangChain Fundamental Concepts

Lecture 5 Getting Started with prompt Template and Chat Prompt Template

Lecture 6 Working with Agents and Tools

Lecture 7 Agents and Tools - Advanced

Lecture 8 Document Loaders and Splitters

Lecture 9 Working with Output Parsers

Lecture 10 Language Embeddings and Vector Databases

Lecture 11 Our first RAG Application using a Vector DB

Lecture 12 Chain Types - Stuff, Map-Reduce and Refine

Lecture 13 LCEL - LangChain Expression Language

Section 3: RAG Applications and Projects

Lecture 14 Working with SQL Data - RAG App

Lecture 15 RAG with Conversational Memory

Lecture 16 Create a CV Upload and CV Search Application

Lecture 17 Create a Website Query Conversational Chatbot - Project

Lecture 18 Analysis of Structured Data from a CSV/Excel using Natural Language

Any Software Developer aspiring to use the power of LLMs to infuse Gen AI features in their Project and Products,Software Developers looking to automate their Software Engineering processes