Building RAG Applications with LangChain and Gen AI Online Course
Building RAG Applications with LangChain and Gen AI Online Course
This course offers a comprehensive introduction to Large Language Models and the LangChain framework, starting with basic concepts like prompt creation and environment setup, followed by practical demonstrations. You’ll explore key LangChain features, such as prompt templates, agents, document loaders, output parsers, and vector databases. Practical RAG (Retrieval-Augmented Generation) applications will be developed throughout the course, including projects involving SQL data, conversational memory, CV search, and website query chatbots. By the end, you’ll be equipped with the skills to build sophisticated AI-driven applications and effectively analyze structured data in real-world scenarios.
Key Benefits
- A thorough introduction to LangChain and Large Language Models
- A detailed exploration of prompts, agents, and tools
- Advanced concepts, including language embeddings and vector databases
Target Audience
This course is for technical professionals and developers who have a foundational understanding of Python and AI concepts. While prior knowledge of natural language processing is beneficial, it is not a prerequisite. The course is ideal for individuals seeking to enhance their expertise in Large Language Models and the LangChain framework.
Learning Objectives
- Design and implement effective prompts for language models
- Develop and leverage agents and tools within the LangChain framework
- Integrate and manage document loaders and output parsers for data processing
- Create and implement language embeddings and manage vector databases
- Build, optimize, and deploy Retrieval-Augmented Generation (RAG) applications
- Analyze structured data through advanced natural language processing techniques
Course Outline
The Building RAG Applications with LangChain and Gen AI Exam covers the following topics -
Module 1 - Course Overview
- Introduction to the Course Content
- Understanding Large Language Models (LLMs)
- Introduction to LangChain Framework
- Fundamentals of Prompting Techniques
- Setting Up the Development Environment
- Installing Necessary Dependencies
- Using Google Gemini LLM in Place of OpenAI GPT
- Code Demonstration - Simple Prompt Creation and Model Chaining
Module 2 - Core Concepts in LangChain
- Getting Started with Prompt Templates and Chat Prompts
- Working with Agents and Their Tools
- Advanced Usage of Agents and Tools
- Introduction to Document Loaders and Splitters
- Understanding Output Parsers
- Exploring Language Embeddings and Vector Databases
- Developing Your First RAG Application Using a Vector Database
- Chain Types - Stuff, Map-Reduce, and Refine
- Understanding LCEL (LangChain Expression Language)
- Building Your First LangChain Application
Module 3 - Building RAG Applications and Projects
- Creating a RAG Application with SQL Data
- Integrating Conversational Memory in RAG Applications
- Building a CV Upload and Search Application
- Developing a Website Query Chatbot Project
- Analyzing Structured Data from CSV/Excel Files Using Natural Language
- Building an Invoice Extraction RAG Application
- Trace and Evaluation Techniques with LangSmith
- Capstone Project for Practical Application
