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Gen Ai Rag Application Development Using Langchain - AD-TEAM - 08-08-2025
![[Image: 78cbb96e6d65cf004bd8b5bf39201f40.jpg]](https://i125.fastpic.org/big/2025/0808/40/78cbb96e6d65cf004bd8b5bf39201f40.jpg)
Gen Ai - Rag Application Development Using Langchain
Published 3/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.02 GB | Duration: 7h 43m
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
![[Image: dIZ6rqdJ_o.jpg]](https://images2.imgbox.com/0f/38/dIZ6rqdJ_o.jpg)
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