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Manas Dasgupta

This course uses Open AI GPT and Google Gemini APIs, LlamaIndex LLM Framework and Vector Databases like ChromaDB and Pinecone, and is intended to help you learn how to build LLM RAG applications through solid conceptual and hands-on sessions. This course covers all the basic aspects to learn LLM RAG apps and Frameworks like Agents, Tools, QueryPipelines, Retrievers, Query Engines in a crisp and clear manner. It also takes a dive into concepts of Language Embeddings and Vector Databases to help you develop efficient semantic search and semantic similarity based RAG Applications. We will also cover multiple Prompt Engineering techniques that will help make your RAG Applications more efficient.

Read more

This course uses Open AI GPT and Google Gemini APIs, LlamaIndex LLM Framework and Vector Databases like ChromaDB and Pinecone, and is intended to help you learn how to build LLM RAG applications through solid conceptual and hands-on sessions. This course covers all the basic aspects to learn LLM RAG apps and Frameworks like Agents, Tools, QueryPipelines, Retrievers, Query Engines in a crisp and clear manner. It also takes a dive into concepts of Language Embeddings and Vector Databases to help you develop efficient semantic search and semantic similarity based RAG Applications. We will also cover multiple Prompt Engineering techniques that will help make your RAG Applications more efficient.

List of Projects/Hands-on included:

Basic RAG: Chat with multiple PDF documents using VectorStore, Retriever, Nodepostprocessor, ResponseSynthesizer and Query Engine.

ReAct Agent: Create a Calculator using a ReAct Agent and Tools.

Document Agent with Dynamic Tools : Create multiple QueryEngineTools dynamically and Orchestrate queries through Agent.

Semantic Similarity: Try Semantic Similarity operations and get Similarity Score. 

Sequential Query Pipeline: Create Simple Sequential Query Pipeline.

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 bots using multiple approaches.

For each project, you will learn:

- The Business Problem

- What LLM and LlamaIndex Components are used

- Analyze outcomes

- What are other similar use cases you can solve with a similar approach.

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What's inside

Learning objectives

  • Fundamentals of llm rag application development
  • Using open ai gpt api to develop rag applications
  • Prompt engineering - write optimized prompts for your rag application
  • Using llamaindex query engines, retrievers and query pipelines
  • Building conversational memory
  • Using data connectors
  • Building smart agents and tools
  • Language embeddings and vector databases
  • Working with sql databases
  • Working with structured data and dataframes in rags
  • Convert your llamaindex rag as a fast api
  • Show more
  • Show less

Syllabus

Introduction
Course Introduction
Introduction to LLMs
Introduction to LlamaIndex
Read more
Introduction to Prompts
Prompts - Advanced
Setup your Development Environment
Your first LlamaIndex Program
Getting Deeper into LlamaIndex
Format Prompt Templates
Conversational Prompts
Semantic Similarity Evaluator
Language Embeddings and Vector Databases
Using a Chroma DB Vector Database
LlamaIndex with SQL Database
LlamaIndex Query Pipelines
Setting up a Simple Sequential Query Pipeline
Setting up a DAG Pipeline
Setting up a Dataframe Pipeline
Working with Agents and Tools
Create a Calculator using a ReAct Agent
Create a Document Agent with Dynamically built Tools
Build a Code Checker with Streamlit UI

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides a detailed overview of the core concepts and components of LLM RAG application development
Covers essential aspects such as agents, tools, query pipelines, retrievers, and query engines for building LLM RAG applications
Focuses on practical implementation with multiple hands-on projects, allowing learners to apply the concepts immediately
Emphasizes the importance of prompt engineering for optimizing LLM RAG applications
Demonstrates the integration of various data sources, including SQL databases and dataframes
Covers advanced concepts like language embeddings and vector databases for efficient semantic search

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Gen AI - RAG Application Development using LlamaIndex with these activities:
Review basic Python syntax
Recall the syntax for working with lists, strings, and dictionaries as these will be fundamental data structures used throughout the course.
Show steps
  • Review lecture notes from a previous Python course or workshop
  • Review Python documentation
Read "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper
Gain a theoretical understanding of natural language processing concepts, which will provide a foundation for understanding LLM RAG.
Show steps
  • Read through the chapters on text processing, machine learning, and natural language understanding
  • Work through the practice exercises to apply your knowledge
Complete the Hugging Face tutorial on Transformers and RAG
Build a foundation for understanding how Transformers and RAG are applied with the support of Hugging Face libraries.
Browse courses on Hugging Face
Show steps
  • Set up your Python environment
  • Follow the Hugging Face tutorial
  • Experiment with different prompts and parameters
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve coding challenges on LeetCode or HackerRank
Strengthen your problem-solving skills and coding proficiency, which will be essential for building robust LLM RAG applications.
Show steps
  • Identify coding challenges that focus on data structures, algorithms, and problem-solving
  • Attempt to solve the challenges independently
  • Review solutions and discuss approaches with peers or mentors
Join a study group
Connect with peers, discuss LLM applications, and share project ideas to enhance your understanding through collaboration.
Show steps
  • Reach out to classmates through online forums or social media
  • Identify students with shared interests and goals
  • Set up regular meetings to discuss course material, explore concepts, and collaborate on projects
Attend a workshop on LLM RAG applications
Gain hands-on experience, learn from experts, and stay updated on industry trends by attending specialized workshops.
Browse courses on RAG
Show steps
  • Research and identify relevant workshops
  • Register for and attend the workshop
  • Engage with the instructors and participants
  • Apply the knowledge and skills gained to your own projects

Career center

Learners who complete Gen AI - RAG Application Development using LlamaIndex will develop knowledge and skills that may be useful to these careers:

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