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.
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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.
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
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- 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
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Syllabus
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Activities
Review basic Python syntax
Show steps
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
Show steps
Gain a theoretical understanding of natural language processing concepts, which will provide a foundation for understanding LLM RAG.
View
Natural Language Processing with Python:...
on Amazon
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review basic Python syntax
Show steps
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
Show steps
Gain a theoretical understanding of natural language processing concepts, which will provide a foundation for understanding LLM RAG.
View
Natural Language Processing with Python:...
on Amazon
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
Show steps
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
Solve coding challenges on LeetCode or HackerRank
Show steps
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
Show steps
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
Show steps
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
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