Building RAG Applications with LangChain and Gen AI Online Course

Building RAG Applications with LangChain and Gen AI Online Course

This course introduces you to the core concepts of Large Language Models and the LangChain framework, starting with prompt engineering and hands-on code demos. You’ll explore prompt templates, agents, tools, document loaders, output parsers, embeddings, and vector databases while building practical projects like RAG applications, SQL data integration, CV search, and website chatbots. By the end, you’ll be equipped to analyze structured data with natural language and build sophisticated AI-powered applications for real-world use.

Who should take this Course?

The Building RAG Applications with LangChain and Gen AI Online Course is ideal for AI developers, data scientists, and software engineers who want to build intelligent Retrieval-Augmented Generation (RAG) applications. It is also suitable for students, researchers, and technology enthusiasts eager to gain hands-on experience with LangChain and generative AI to create advanced, data-driven applications that leverage large language models effectively.

What you will learn

• Formulate and implement prompts for language models
• Develop and utilize agents and tools within the LangChain framework
• Integrate and manage document loaders and output parsers
• Create and utilize language embeddings and vector databases
• Build and deploy Retrieval-Augmented Generation (RAG) applications
• Analyze structured data using natural language processing techniques

Course Outline

Introduction

• Introduction to the Course
• Introduction to Large Language Models
• Introduction to LangChain Framework
• Introduction to Prompts
• Environment Setup
• Installing Dependencies
• Using Google Gemini LLM (instead of OpenAI GPT)
• Code Demo - Simple ways of forming a Prompt and using it to Chain with a Model

LangChain Fundamental Concepts

• Getting Started with Prompt Template and Chat Prompt Template
• Working with Agents and Tools
• Agents and Tools - Advanced
• Document Loaders and Splitters
• Working with Output Parsers
• Language Embeddings and Vector Databases
• Our First RAG Application using a Vector DB
• Chain Types - Stuff, Map-Reduce and Refine
• LCEL - LangChain Expression Language
• Our First Langchain Program

RAG Applications and Projects

• Working with SQL Data - RAG App
• RAG with Conversational Memory
• Create a CV Upload and CV Search Application
• Create a Website Query Conversational Chatbot - Project
• Analysis of Structured Data from a CSV/Excel using Natural Language
• Invoice Extraction RAG Application
• Traces and Evaluation with LangSmith
• Capstone Project