Gen AI - LLM RAG Two in One - LangChain + LlamaIndex

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1 - Introduction

1 -Introduction to the Course.mp4

04:46

2 -Introduction to Large Language Models (LLMs).mp4

32:31

3 -Introduction to Prompt Engineering.mp4

17:33

4 -Prompts Advanced.mp4

18:53

2 - Starting with LangChain

1 -Introduction to LangChain.mp4

22:58

2 -LangChain Environment Setup.mp4

18:58

2 -Using+Google+Gemini+Model.pdf

3 -Installing Dependencies.mp4

05:24

3 -LangChain-Course-Dependencies.pdf

4 -Using Google Gemini LLM.mp4

18:27

4 -Using+Google+Gemini+Model.pdf

4 -blog-writer-gemini.zip

4 -gemini-try.zip

5 -GenAI-LLM-LC-Code.zip

5 -Our First LangChain Program.mp4

13:10

3 - Learn LangChain through Projects

1 -Working with SQL Data - RAG Application.mp4

11:37

2 -Create a CV Upload and Search Application.mp4

22:10

3 -Create an Invoice Extract RAG Application.mp4

24:08

4 -Create a Conversational Chatbot for HR Policy Queries.mp4

22:25

4 -wget-setup-windows.pdf

5 -Analysis of Structured Data using Natural Language.mp4

26:32

4 - Getting Started with LlamaIndex

1 -Introduction to LlamaIndex.mp4

39:52

2 -LlamaIndex setup.mp4

47:48

2 -Setup-LlamaIndex-RAGs-1.pdf

3 -LlamaIndex-Code-Data.zip

3 -Our First LlamaIndex Program.mp4

32:20

5 - Learn LlamaIndex through Projects

1 -RAG App using Chroma DB Vector Database.mp4

19:51

2 -LlamaIndex RAG with SQL Database.mp4

24:06

3 -LlamaIndex Query Pipelines.mp4

15:47

4 -LlamaIndex Sequential Query Pipeline.mp4

06:38

5 -LlamaIndex Complex DAG Pipeline.mp4

21:27

6 -Setting up a DataFrame Pipeline.mp4

22:40

7 -Working with Agents and Tools.mp4

14:05

8 -Create a Calculator RAG App using ReAct Agents.mp4

12:37

9 -Create a Document Agent with Dynamically built Tools.mp4

21:51

10 -Create a Code Checker RAG App.mp4

14:36

10 -code-checker-li-st.zip

Description

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

Who is this for?

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

What You Need to Know?

Python Programming Knowledge

More details

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 DB
Create a CV Upload and Semantic CV Search App
Invoice Extraction RAG App
Create a Structured Data Analytics App that uses Natural Language Queries
ReAct Agent: Create a Calculator App using a ReAct Agent and Tools
Document Agent with Dynamic Tools: Create multiple QueryEngineTools dynamically and orchestrate queries through Agents
Sequential Query Pipeline: Create Simple Sequential Query Pipelines
DAG Pipeline: Develop complex DAG Pipelines
Dataframe Pipeline: Develop complex Dataframe Analysis Pipelines with Pandas Output Parser and Response Synthesizer
Working with SQL Databases: Develop SQL Database ingestion Bot

This 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.
Who this course is for:
Software Developers, Data Scientists, ML Engineers, DevOps Engineers, Support Engineers, Test / QA Engineers

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