OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.

Getting started with Gen AI using LlamaIndex for Beginners

Praveen Singh

Welcome to this introductory course on LlamaIndex, a powerful tool for indexing and querying data using large language models such as OpenAI's API.

In this course, you will learn the basics of LlamaIndex and how to use it to index your data for various natural language processing tasks such as summarization, and question answering. You will also learn how to perform queries on your indexed data and how to integrate LlamaIndex with different LLM models.

Read more

Welcome to this introductory course on LlamaIndex, a powerful tool for indexing and querying data using large language models such as OpenAI's API.

In this course, you will learn the basics of LlamaIndex and how to use it to index your data for various natural language processing tasks such as summarization, and question answering. You will also learn how to perform queries on your indexed data and how to integrate LlamaIndex with different LLM models.

The course is designed for beginners with some prior knowledge of Python programming. You should be comfortable writing and understanding basic Python code and be familiar with package installers such as pip and development environments such as Visual Studio Code.

The course is designed for beginners and no prior knowledge of LlamaIndex or natural language processing is required. Through a series of hands-on exercises and practical examples, you will gain a solid understanding of LlamaIndex and its capabilities.

By the end of this course, you will be able to:

  • Understand the basics of LlamaIndex and its architecture

  • Index your data for various natural language processing tasks

  • Perform queries on your indexed data

  • Learn about the Indexing storage

  • Learn to pass custom LLM model

  • Learn to integrate with Vector Database

  • Learn to integrate with UI platforms (Streamlit, Chainlit etc..)

Enroll now and start your journey with LlamaIndex.

The LLM space is continuously evolving and so does the underlying frameworks, so don't be surprised with new additions . Just stay tuned .

Enroll now

What's inside

Learning objectives

  • Become proficient in llamaindex
  • Learn to query your custom documents using llamaindex
  • Get the understanding of different aspects of llamaindex
  • Get the understanding of different concepts of large language models
  • Integrate llamaindex with vector database
  • Integrate llamaindex with ui (streamlit etc..)

Syllabus

Overview and Concepts
Introduction to Large Language Model
How to connect to external Data ?
What is LlamaIndex ?
Read more
Overview of required steps to build apps using LlamaIndex
What is In-Context learning ?
Difference between In-Context Learning and Fine-Tuning ?
Pricing
How does LlamaIndex applications work internally ?
Why is Indexing required in LLM application ?
Keep yourself updated with LlamaIndex
LlamaIndex : Blogs, Docs and Discord
Environment setup
OpenAI models and Default in LlamaIndex
How to get OpenAI API key?
Different ways of setting up OpenAI API key
Package Installation : LlamaIndex
Notes : Package Installation : OpenAI
How to connect and query different File formats ?
Base of LlamaIndex : Document and Nodes
Overview of SimpleDirectoryReader and VectorStoreIndex
(Hands-On) SimpleDirectoryReader and VectorStoreIndex
(Hands-on) How to query multiple Text files ?
(Hands-on) How to query CSV files ?
(Hands-on) How to query PDF files ?
(Hands-on) How to query Excel files ?
(Hands-on) How to query DOC files ?
Overview of LlamaParse and where it helps ?
(Hands-on) How to use LlamaParse ?
Index Management
(Hands-on) How to persist indexes ?
Types of Indexes in LlamaIndex
(Hands-On) Overview on performing Insert, Delete, Update on Indexes
(Hands-On) How to refresh indexes in real world with multiple folders
Customizing LLM models in LlamaIndex
Overview of Customization
(Hands-on) Changing underlying LLM model
(Hands-on) Customizing number of output tokens
(Hands-on) Customizing Chunk Size & Overlap Parameters
[Optional] All possible Customization in one go
Enable Streaming and Chat Response
Streaming response overview
(Hands-on) Enable Streaming response
Chat Engine : High Level Flow
(Hands-On) Chat Engine : The React mode
(Hands-On) Chat Engine : The Condense mode
Prompt Engineering
Different Prompts in LlamaIndex
(Hands-On) Customizing Prompt
Exposing LlamaIndex app as an API
(Hands-on) Expose API using Flask : Integration with endpoint part 1
(Hands-on) Expose API using Flask : Integration with endpoint part 2
(Hands-on) Expose API using Flask : Integration with endpoint part 3
Expose API using FastAPI : Pydantic and your first FastAPI application
(Hands-on) Expose API using FastAPI : Integrate app with FastAPI
The concept of Vector Databases
What is VectorDB ?
(Hands-On) Build LLM apps using ChromaDB
(Hands-On) Use MongoDB as Storage : Option#1
(Hands-On) Use MongoDB as Storage : Option#2
Structured Response from LLM models
Structured Output using LangchainOutputParser
LlamaIndex Integration with Other models
Integration with Ollama and Groq
Build applications using Agents
Overview of Agents in LlamaIndex
FunctionTool : How to convert custom Functions into Agent ?
QueryEngineTool : Query your documents using Agent
ToolSpecs : Pre-built Agent Tools
How to get Structured Output from Image ?
How to get Structured Output from Images ?
Enable UI for your LLM applications
UI using chainlit : overview
(Hands-On) Build UI using chainlit
UI using Streamlit : A Simple Webapp
UI using Streamlit : Chat application with streaming response
UI Using Streamlit : Chat application without streaming response
Semantic Mapping : Match text based on their Semantic Meaning
Semantic Mapping : Match Texts based on their Semantic Meaning
Token Prediction and Cost Analysis
High Level Overview : Token Predictor and Cost Analysis
(Hands- On) Identify tokens using Token Predictor

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides hands-on exercises and practical examples, which allows learners to gain a solid understanding of LlamaIndex and its capabilities through active participation
Covers integration with UI platforms like Streamlit and Chainlit, which enables learners to build interactive applications using LlamaIndex and large language models
Explores the concept of Vector Databases and their integration with LlamaIndex, which is essential for building advanced LLM-powered applications that require efficient data retrieval
Requires familiarity with Python, pip, and VS Code, which may pose a barrier to entry for individuals without prior programming experience or access to these tools
Teaches integration with OpenAI models, which may require learners to obtain an OpenAI API key and incur costs associated with using the OpenAI API
Focuses on LlamaIndex, which is continuously evolving, so learners should be prepared to adapt to new additions and changes in the framework

Save this course

Save Getting started with Gen AI using LlamaIndex for Beginners to your list so you can find it easily later:
Save

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 Getting started with Gen AI using LlamaIndex for Beginners with these activities:
Review Python Fundamentals
Strengthen your Python programming foundation to better understand and implement LlamaIndex examples and exercises.
Browse courses on Python Basics
Show steps
  • Review data types, control flow, and functions.
  • Practice writing simple Python scripts.
  • Familiarize yourself with package installers like pip.
Read 'Natural Language Processing with Python'
Gain a deeper understanding of the NLP concepts underlying LlamaIndex by studying a foundational NLP text.
View Natural Language Processing with Python:... on Amazon
Show steps
  • Read the chapters on text processing and information extraction.
  • Experiment with the NLTK library for text analysis.
Follow LlamaIndex Tutorials
Reinforce your understanding of LlamaIndex by working through official tutorials and examples.
Show steps
  • Work through the official LlamaIndex documentation.
  • Follow tutorials on indexing and querying data.
  • Experiment with different LLM models and vector databases.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Building Applications with Vector Databases'
Gain a deeper understanding of vector databases and their role in LlamaIndex by studying a dedicated text.
View Melania on Amazon
Show steps
  • Read the chapters on vector embeddings and similarity search.
  • Experiment with different vector database implementations.
Build a Q&A App with LlamaIndex
Solidify your LlamaIndex skills by building a practical question-answering application using your own data.
Show steps
  • Choose a dataset of documents to index.
  • Implement indexing and querying using LlamaIndex.
  • Integrate with a UI platform like Streamlit or Chainlit.
  • Deploy your application and test its functionality.
Contribute to LlamaIndex
Deepen your understanding of LlamaIndex by contributing to the open-source project.
Show steps
  • Explore the LlamaIndex GitHub repository.
  • Identify bugs or areas for improvement.
  • Submit pull requests with bug fixes or new features.
Write a Blog Post on LlamaIndex
Share your knowledge and insights about LlamaIndex by writing a blog post on a specific topic or application.
Show steps
  • Choose a topic related to LlamaIndex.
  • Research and gather information on your chosen topic.
  • Write a clear and concise blog post explaining the topic.
  • Publish your blog post on a platform like Medium or your own website.

Career center

Learners who complete Getting started with Gen AI using LlamaIndex for Beginners will develop knowledge and skills that may be useful to these careers:
Generative AI Engineer
A generative AI engineer focuses on developing and implementing AI models that can generate new content, such as text, images, or audio. This course on LlamaIndex is highly relevant to this role, as it provides the skills needed to work with large language models for natural language processing tasks. You will learn how to index data, perform queries, and integrate LlamaIndex with different LLM models. The course's hands-on exercises, including customizing prompts and building UIs, are directly applicable to developing generative AI applications. For anyone looking to specialize as a generative AI engineer, this course offers practical knowledge and experience with LlamaIndex, a crucial tool in the field.
See salaries and explore the career path for Generative AI Engineer
Natural Language Processing Engineer
A natural language processing engineer specializes in developing algorithms and models that enable computers to understand and process human language. The course’s focus on LlamaIndex directly aligns with the core responsibilities of this role. The course introduces the basics of LlamaIndex and teaches how to index data for NLP tasks, perform queries, and integrate with various LLM models, all essential skills for an engineer in this field. The hands-on exercises, including working with different file formats and customizing prompts, provide practical experience that is highly relevant. For anyone looking to excel as a natural language processing engineer, this course provides a solid foundation in using LlamaIndex to build and deploy language-centric applications.
See salaries and explore the career path for Natural Language Processing Engineer
Prompt Engineer
A prompt engineer crafts effective prompts for large language models to elicit desired outputs. This course on LlamaIndex is invaluable for prompt engineers, as it delves into customizing prompts and understanding how to optimize them for various NLP tasks. The course's hands-on exercises, including customizing prompts and experimenting with different models, provide practical experience in prompt engineering. Furthermore, the knowledge of LlamaIndex's capabilities and integration with different LLMs is crucial for crafting prompts that leverage the full potential of these models. For those looking to specialize in prompt engineering, this course offers a comprehensive understanding of LlamaIndex and its role in optimizing language model outputs.
See salaries and explore the career path for Prompt Engineer
Information Retrieval Specialist
An information retrieval specialist focuses on designing and implementing systems for efficient information access, especially within large datasets. This course on LlamaIndex is directly relevant, teaching you how to index and query data effectively using large language models. The course emphasizes indexing techniques, different indexing types, and performing queries, which are all core skills for this specialist. The hands-on sessions on querying various file formats and managing indexes will be useful. For anyone aspiring to be an information retrieval specialist, this course offers a solid foundation in applying LlamaIndex to indexing and querying tasks.
See salaries and explore the career path for Information Retrieval Specialist
AI Application Developer
An AI application developer creates and implements artificial intelligence solutions for various business needs. This course about LlamaIndex provides the foundational knowledge required to develop AI applications leveraging large language models. Through learning how to index data, perform queries, and integrate LlamaIndex with UI platforms like Streamlit and Chainlit, one gains the ability to build interactive AI applications. The course's hands-on approach, including exposing LlamaIndex apps as APIs and building UIs, is invaluable for any AI application developer. This course is particularly beneficial for those aiming to create AI-powered applications that utilize natural language processing, offering practical skills and insights into LlamaIndex's capabilities.
See salaries and explore the career path for AI Application Developer
Machine Learning Engineer
The machine learning engineer designs, develops, and deploys machine learning models and systems. This course on LlamaIndex, with its detailed exploration of indexing and querying data using large language models, is useful for machine learning engineers. By learning how to integrate LlamaIndex with different LLM models and vector databases, one prepares to build sophisticated NLP pipelines. The hands-on exercises in the course, such as customizing LLM models and enabling streaming responses, equip machine learning engineers with practical skills for optimizing and deploying language-based applications. This course is beneficial for machine learning engineers who want to master the application of large language models in real-world scenarios, providing them with a solid understanding of LlamaIndex and its capabilities.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
A data scientist leverages programming and analytical skills to extract meaningful insights from complex datasets. This course, focused on LlamaIndex, helps build a foundation for working with large language models, a critical component in modern data science. This course provides hands-on experience in indexing data for natural language processing tasks and performing queries, which directly translates to a data scientist's ability to analyze unstructured text data and derive valuable conclusions. The course's coverage of integrating LlamaIndex with vector databases and UI platforms (Streamlit, Chainlit) further enhances a data scientist's toolkit for building and deploying data-driven applications. For data scientists seeking to incorporate cutting-edge language models into their workflows, this course offers practical knowledge and skills.
See salaries and explore the career path for Data Scientist
AI Trainer
AI trainers refine and improve the performance of AI models through careful analysis, feedback, and iterative adjustments. This course on LlamaIndex helps to understand how to customize LLM models and tailor their behavior. By learning how to integrate LlamaIndex with different LLM models and customize prompts, AI trainers can more effectively fine-tune model outputs. The hands-on exercises, including changing the underlying LLM model and customizing output tokens, provide practical experience in model training. For AI trainers seeking to optimize language models for specific tasks, this course offers knowledge and skills.
See salaries and explore the career path for AI Trainer
Software Engineer
A software engineer designs, develops, and maintains software systems. With the increasing integration of AI into software applications, a software engineer with knowledge of tools like LlamaIndex is highly valuable. This course provides practical skills in indexing data, performing queries, and integrating LlamaIndex with various LLM models. By learning how to expose LlamaIndex apps as APIs and build UIs, software engineers can create AI-powered features for their applications. For software engineers looking to incorporate natural language processing capabilities into their projects, this course offers a solid foundation in LlamaIndex and its applications.
See salaries and explore the career path for Software Engineer
Knowledge Engineer
A knowledge engineer designs and develops systems that capture, represent, and reason with knowledge. This course may be helpful to knowledge engineers as it covers LlamaIndex, a tool that can be used to index and query data using large language models. By learning how to connect to external data sources, customize LLM models, and build UIs, knowledge engineers can design more effective knowledge management systems. The course's hands-on exercises, including working with different file formats and customizing prompts, provide practical experience that is relevant. This course helps expand their toolkit for building and deploying knowledge-centric applications.
See salaries and explore the career path for Knowledge Engineer
AI Research Scientist
AI research scientists conduct research to advance the field of artificial intelligence. They often require a Ph.D. This course provides a practical understanding of LlamaIndex, a tool that can be used in AI research projects involving large language models. While research often involves theoretical work, practical knowledge of tools like LlamaIndex is essential for experimentation and prototyping new ideas. The course's coverage of indexing, querying, and integrating with different LLM models helps stay up-to-date with the latest advancements. This course may be helpful for AI research scientists as it provides a hands-on perspective on using LlamaIndex in NLP research, which can inform and enhance their theoretical work.
See salaries and explore the career path for AI Research Scientist
Data Architect
A data architect designs and manages an organization's data infrastructure, ensuring data is accessible, secure, and efficiently managed. This course on LlamaIndex may be useful as it explores the integration of LlamaIndex with vector databases and other data storage solutions. Understanding how to index and query data using large language models can inform the design of data architectures that support AI-powered applications. The course's coverage of different file formats and data sources helps data architects design flexible and scalable data solutions. This is particularly useful for data architects as it helps in designing infrastructure to support AI-driven data processing and analysis.
See salaries and explore the career path for Data Architect
Data Analyst
A data analyst examines data using statistical tools to identify trends and insights. While this role typically deals with structured data, the ability to analyze unstructured text data is becoming increasingly valuable. This course on LlamaIndex helps build a foundation for working with large language models and indexing data for NLP tasks. By learning how to perform queries and extract information from text data, one enhances their analytical capabilities. The course may be useful for data analysts who want to expand their skill set to include natural language processing. The course can help analyze textual datasets and derive meaningful insights, complementing their existing expertise in structured data analysis.
See salaries and explore the career path for Data Analyst
Solutions Architect
A solutions architect designs and oversees the implementation of technology solutions to address business problems. Given the rise of AI and natural language processing, understanding how to integrate these technologies is crucial. This course on LlamaIndex may be useful as it provides a comprehensive overview of indexing, querying, and integrating large language models into applications. By learning how to connect to external data sources, customize LLM models, and build UIs, solutions architects can design robust AI-powered solutions. This course helps broaden their understanding of LlamaIndex, enabling them to make informed decisions about incorporating NLP into their architectural designs.
See salaries and explore the career path for Solutions Architect
Technical Consultant
A technical consultant provides expert advice and guidance to organizations on technology-related issues. As AI and NLP become more prevalent, technical consultants need to understand the capabilities and applications of tools like LlamaIndex. This course may be useful as it provides a practical introduction to LlamaIndex, covering topics such as indexing data, performing queries, and integrating with various LLM models. The course's hands-on exercises, including building UIs and exposing APIs, can help technical consultants demonstrate the value of LlamaIndex to their clients. This enables to provide informed recommendations and guidance on implementing NLP solutions.
See salaries and explore the career path for Technical Consultant

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Getting started with Gen AI using LlamaIndex for Beginners.
Cover image
Think and Grow Rich - English : For Personal Growth...
Save
Provides a comprehensive guide to building applications using vector databases, a key component of LlamaIndex. It covers topics such as vector embeddings, similarity search, and indexing techniques. It valuable resource for understanding how LlamaIndex integrates with vector databases to perform efficient data retrieval. This book is commonly used by industry professionals working with vector databases.
Melania
Hardcover
$$
From Here to the Great Unknown: Oprah's Book Club:...
Hardcover
$$
War
Hardcover
$$
The Message
Hardcover
$$
Good Energy: The Surprising Connection Between...
Hardcover
$$
Be Ready When the Luck Happens: A Memoir
Hardcover
$$
The Anxious Generation: How the Great Rewiring of...
Hardcover
$$
Onyx Storm (Deluxe Limited Edition) (The Empyrean,...
Hardcover
$$
Spooky Cutie: Coloring Book for Adults and Teens...
Paperback
$
The Leaf Thief: (The Perfect Fall Book for Children...
Paperback
$$
Cover image
Natural Language Processing with Python
Save
Provides a comprehensive introduction to NLP concepts and techniques using Python. It covers topics such as text processing, classification, and information extraction, which are relevant to understanding how LlamaIndex processes and queries data. While not strictly required, it offers valuable background knowledge for those new to NLP. This book is commonly used in academic settings for NLP courses.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
(中文) Python Natural Language Processing(Chinese Edition)
Paperback
$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Similar courses are unavailable at this time. Please try again later.
Effort
5.5 total hours
Via
Udemy
Instructor
Praveen Singh
Language
English
Good to know
Provides hands-on exercises and practical examples, which allows learners to gain a solid understanding of LlamaIndex and its capabilities through active participation
Covers integration with UI platforms like Streamlit and Chainlit, which enables learners to build interactive applications using LlamaIndex and large language models
Explores the concept of Vector Databases and their integration with LlamaIndex, which is essential for building advanced LLM-powered applications that require efficient data retrieval
Requires familiarity with Python, pip, and VS Code, which may pose a barrier to entry for individuals without prior programming experience or access to these tools
Teaches integration with OpenAI models, which may require learners to obtain an OpenAI API key and incur costs associated with using the OpenAI API
Focuses on LlamaIndex, which is continuously evolving, so learners should be prepared to adapt to new additions and changes in the framework
Share and help others discover this course.
Facebook LinkedIn X Reddit Link Email
Don't miss out
Enroll today to gain access to this course
Enroll in this course
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser