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Damien Benveniste
Welcome to the Introduction to LangChain course. Very recently, we saw a revolution with the advent of Large Language Models. It is rare that something changes the world of Machine Learning that much, and the hype around LLM is real. That's something that very few experts predicted, and it's essential to be prepared for the future.
Read more
Welcome to the Introduction to LangChain course. Very recently, we saw a revolution with the advent of Large Language Models. It is rare that something changes the world of Machine Learning that much, and the hype around LLM is real. That's something that very few experts predicted, and it's essential to be prepared for the future.
Read more
Welcome to the Introduction to LangChain course. Very recently, we saw a revolution with the advent of Large Language Models. It is rare that something changes the world of Machine Learning that much, and the hype around LLM is real. That's something that very few experts predicted, and it's essential to be prepared for the future.
LangChain is an amazing tool that democratizes machine learning for everybody. With LangChain, every software engineer can use machine learning and build applications with it. Prior to LangChain and LLMs, you needed to be an expert in the field. Now, you can build an application with a couple of lines of code. Think about language models as a layer between humans and software. LangChain is a tool that allows the integration of LLMs within a larger software.
Topics covered in that course:
LangChain Basics
Loading and Summarizing Data
Prompt Engineering Fundamentals
Vector Database Basics
Retrieval Augmented Generation
RAG Optimization and Multimodal RAG
Augmenting LLMs with a Graph Database
Augmenting LLMs with tools
How to Build a Smart Voice Assistant
How to Automate Writing Novels
How to Automate Writing Software
The course is very hands-on. We will work on many examples to build your intuition on the different concepts we will address in this course. By the end of the course, you will be able to build complex software applications powered by Large Language Models.
Warning: during the course, I used a lot of the OpenAI models through their API. If you choose to use the OpenAI API as well, be aware that this will generate additional costs. I expect that reproducing all the examples in the course should not require more than $50 in OpenAI credits. However, all the examples can be reproduced for free if you choose to use open-source LLMs.
Register for this course and see more details by visiting:
OpenCourser.com/course/m1l1nu/introduction
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What's inside
Learning objectives
- Build software applications with large language models
- Learn how to augment llms with tools and databases
- Learn how to connect llms to external data
- Learn the fundamentals of prompt engineering
- Learn the fundamentals of vector databases
- Learn the fundamentals of retrieval augmented generation
- Langchain: models, chains, prompts, memory, vector stores, agents!
- Build software applications with large language models
- Learn how to augment llms with tools and databases
- Learn how to connect llms to external data
- Learn the fundamentals of prompt engineering
- Learn the fundamentals of vector databases
- Learn the fundamentals of retrieval augmented generation
- Langchain: models, chains, prompts, memory, vector stores, agents!
Syllabus
Introduction
Introduction to the course
Course structure
Setting up your Jupyter Notebook (optional)
Read more
Syllabus
Introduction
Introduction to the course
Course structure
Setting up your Jupyter Notebook (optional)
Read more
LangChain Basics
Text
OpenAI package (temporary fix)
LLMs
Chains
Prompt Templates
Output parsers
Simple Sequence
Written material
Outro
Loading and Summarizing Data
Loading Data
Summary strategies
Summarization examples
Prompt Engineering Fundamentals
Elements of a Prompt
Few-Shot Learning
Memetic Proxy
Chain of Thought
Self-Consistency
Inception
Self-Ask
ReAct
Plan and Execute
Vector Database Basics
Intro
Why Vector Databases?
Similarity Metrics
Why do we need Indexing?
Product Quantization
Locality Sensitive-Hashing
Navigable Small World
Hierarchical Navigable Small World
Maximum Marginal Relevance
We learn how to augment LLMs with additional data coming from a database
Indexing data
Loading data into a vector database
Providing sources
Indexing a website
Indexing a GitHub repository
The Stuff Strategy
The Map-Reduce Strategy
The Refine strategy
The Map-Rerank strategy
RAG optimization and Multimodal RAG
Multi-Vector Retriever
Hypothetical Queries
Parsing a Multimodal Document
Summarizing the Data
Describing Images with LlaVA
Index the Data into a Database
Finalizing the RAG Pipeline
Augmenting LLMs with a Graph Database
What is a Knowledge Base
Getting the Data
Create the Graph Representation
Augmenting LLMs with a Knowledge Base
Using the Diffbot Graph Transformer
Creating a Local Graph Database
Augmenting an LLM with the Graph Database
Augmenting LLMs with Tools
What is an Agent?
Agent Example
Dissecting the Iterative Process
The Different Tools
Building Custom Tools
How to build a Smart Voice Assistant
What are we building
Setting up the Project
From Speech to Text
From Text to Speech
Building a Conversational Agent
Augmenting the Agent with Tools
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by an expert in the field of natural language processing
Covers a wide range of topics related to large language models
Hands-on and interactive, allowing learners to gain practical experience
Provides a strong foundation for beginners in the field of large language models
Requires some prior knowledge of machine learning and programming
May require additional costs for using OpenAI models
Save this course
Save Introduction to LangChain to your list so you can find it easily later:
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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 Introduction to LangChain with these
activities:
Review LLMs
Show steps
Familiarize yourself with LLMs to build a base knowledge.
Browse courses on
Large Language Models
Show steps
Read "The AI Book" by Peter Norvig
Show steps
Expand your knowledge of artificial intelligence and its applications.
View
Data Science in Context: Foundations,...
on Amazon
Show steps
Practice Summarizing Text
Show steps
Exercises to reinforce text summarization skills taught in the course.
Browse courses on
Summarization
Show steps
-
Select a piece of text to summarize.
-
Read the text and identify the main points.
-
Write a summary of the text, capturing the main ideas and key details.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Additional LangChain Models
Show steps
Gain deeper understanding of LangChain's capabilities by exploring and experimenting with additional models.
Browse courses on
LangChain
Show steps
-
Read the documentation and tutorials on the available LangChain models.
-
Choose a model to experiment with.
-
Develop test cases to evaluate the model's performance.
-
Analyze the results and learn from the model's behavior.
Try LangChain with OpenAI API
Show steps
Extend your knowledge of LangChain by experimenting with the OpenAI API.
Browse courses on
LangChain
Show steps
-
Set up an OpenAI API account.
-
Find appropriate OpenAI models to use with LangChain.
-
Implement the integration between LangChain and OpenAI models.
-
Test and evaluate the performance of your integration.
Build a Simple Text Summarization Tool
Show steps
Apply your skills by creating a practical tool that can summarize text automatically.
Browse courses on
Summarization
Show steps
-
Design the user interface and functionality of the tool.
-
Implement the text summarization algorithm using LangChain.
-
Test the tool with different types of text and evaluate its performance.
-
Deploy the tool and make it accessible to others.
Contribute to a LangChain Open-Source Project
Show steps
Gain hands-on experience and contribute to the LangChain community by participating in an open-source project.
Browse courses on
LangChain
Show steps
-
Find a LangChain open-source project that aligns with your interests and skills.
-
Join the project's community and familiarize yourself with its goals, codebase, and contribution guidelines.
-
Identify a task or feature that you can contribute to.
-
Fork the project's repository, make your changes, and submit a pull request.
-
Collaborate with other contributors to refine and merge your changes.
Write a Blog Post on LangChain's Impact on Machine Learning
Show steps
Share your insights and contribute to the community by writing about LangChain's impact on machine learning.
Browse courses on
LangChain
Show steps
-
Research and gather information about LangChain and its applications in machine learning.
-
Outline the key points and arguments you want to make in your blog post.
-
Write the first draft of your blog post.
-
Edit and refine your blog post, ensuring clarity, conciseness, and accuracy.
-
Publish your blog post and promote it through social media and other channels.
Review LLMs
Show steps
Familiarize yourself with LLMs to build a base knowledge.
Browse courses on
Large Language Models
Show steps
Show steps
Show steps
Read "The AI Book" by Peter Norvig
Show steps
Expand your knowledge of artificial intelligence and its applications.
View
Data Science in Context: Foundations,...
on Amazon
Show steps
Show steps
Show steps
Practice Summarizing Text
Show steps
Exercises to reinforce text summarization skills taught in the course.
Browse courses on
Summarization
Show steps
Show steps
Show steps
- Select a piece of text to summarize.
- Read the text and identify the main points.
- Write a summary of the text, capturing the main ideas and key details.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Additional LangChain Models
Show steps
Gain deeper understanding of LangChain's capabilities by exploring and experimenting with additional models.
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Read the documentation and tutorials on the available LangChain models.
- Choose a model to experiment with.
- Develop test cases to evaluate the model's performance.
- Analyze the results and learn from the model's behavior.
Try LangChain with OpenAI API
Show steps
Extend your knowledge of LangChain by experimenting with the OpenAI API.
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Set up an OpenAI API account.
- Find appropriate OpenAI models to use with LangChain.
- Implement the integration between LangChain and OpenAI models.
- Test and evaluate the performance of your integration.
Build a Simple Text Summarization Tool
Show steps
Apply your skills by creating a practical tool that can summarize text automatically.
Browse courses on
Summarization
Show steps
Show steps
Show steps
- Design the user interface and functionality of the tool.
- Implement the text summarization algorithm using LangChain.
- Test the tool with different types of text and evaluate its performance.
- Deploy the tool and make it accessible to others.
Contribute to a LangChain Open-Source Project
Show steps
Gain hands-on experience and contribute to the LangChain community by participating in an open-source project.
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Find a LangChain open-source project that aligns with your interests and skills.
- Join the project's community and familiarize yourself with its goals, codebase, and contribution guidelines.
- Identify a task or feature that you can contribute to.
- Fork the project's repository, make your changes, and submit a pull request.
- Collaborate with other contributors to refine and merge your changes.
Write a Blog Post on LangChain's Impact on Machine Learning
Show steps
Share your insights and contribute to the community by writing about LangChain's impact on machine learning.
Browse courses on
LangChain
Show steps
Show steps
Show steps
- Research and gather information about LangChain and its applications in machine learning.
- Outline the key points and arguments you want to make in your blog post.
- Write the first draft of your blog post.
- Edit and refine your blog post, ensuring clarity, conciseness, and accuracy.
- Publish your blog post and promote it through social media and other channels.
Career center
Learners who complete Introduction to LangChain will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of programming, statistics, and machine learning to extract insights from data. This course can help you build a strong foundation in machine learning, which is a key skill for Data Scientists. You will learn how to use machine learning techniques to solve real-world problems, such as fraud detection and customer segmentation.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course can help you build a strong foundation in machine learning, which is a key skill for Machine Learning Engineers. You will learn how to use machine learning techniques to solve real-world problems, such as fraud detection and customer segmentation.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you build a strong foundation in machine learning, which is a key skill for Software Engineers. You will learn how to use machine learning techniques to build software applications that are more intelligent and efficient.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. This course can help you build a strong foundation in machine learning, which is a key skill for Data Analysts. You will learn how to use machine learning techniques to analyze data and identify trends and patterns.
Sales Analyst
Sales Analyst
Sales Analysts help businesses understand their sales performance and identify opportunities for improvement. This course can help you build a strong foundation in machine learning, which is a key skill for Sales Analysts. You will learn how to use machine learning techniques to analyze sales data and identify trends and patterns.
Business Analyst
Business Analyst
Business Analysts help businesses improve their operations and make better decisions. This course can help you build a strong foundation in machine learning, which is a key skill for Business Analysts. You will learn how to use machine learning techniques to analyze data and identify opportunities for improvement.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course can help you build a strong foundation in machine learning, which is a key skill for Operations Research Analysts. You will learn how to use machine learning techniques to develop models that can optimize business processes.
Marketing Analyst
Marketing Analyst
Marketing Analysts help businesses understand their customers and develop effective marketing campaigns. This course can help you build a strong foundation in machine learning, which is a key skill for Marketing Analysts. You will learn how to use machine learning techniques to analyze customer data and identify opportunities for growth.
Risk Analyst
Risk Analyst
Risk Analysts help businesses identify and manage risks. This course can help you build a strong foundation in machine learning, which is a key skill for Risk Analysts. You will learn how to use machine learning techniques to develop models that can predict risks and identify opportunities for mitigation.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course can help you build a strong foundation in machine learning, which is a key skill for Product Managers. You will learn how to use machine learning techniques to identify customer needs and develop products that meet those needs.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to evaluate financial risks and opportunities. This course can help you build a strong foundation in machine learning, which is a key skill for Quantitative Analysts. You will learn how to use machine learning techniques to develop models that can predict financial outcomes.
Financial Analyst
Financial Analyst
Financial Analysts help businesses make investment decisions. This course can help you build a strong foundation in machine learning, which is a key skill for Financial Analysts. You will learn how to use machine learning techniques to analyze financial data and identify investment opportunities.
Consultant
Consultant
Consultants help businesses solve problems and improve their performance. This course can help you build a strong foundation in machine learning, which is a key skill for Consultants. You will learn how to use machine learning techniques to analyze data and identify opportunities for improvement.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course can help you build a strong foundation in machine learning, which is a key skill for Statisticians. You will learn how to use machine learning techniques to analyze data and identify trends and patterns.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course can help you build a strong foundation in machine learning, which is a key skill for Actuaries. You will learn how to use machine learning techniques to develop models that can predict risks and identify opportunities for mitigation.
For more career information including salaries, visit:
OpenCourser.com/course/m1l1nu/introduction
Reading list
We've selected ten 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
Introduction to LangChain.
Save
Classic textbook on deep learning, covering a wide range of topics from neural networks to deep learning architectures. It valuable resource for readers who want to learn the fundamental concepts and algorithms of deep learning.
Classic textbook on statistical learning, covering a wide range of topics from supervised learning to unsupervised learning. It valuable resource for readers who want to learn the fundamental concepts and algorithms of statistical learning.
Classic textbook on reinforcement learning, covering a wide range of topics from Markov decision processes to deep reinforcement learning. It valuable resource for readers who want to learn the fundamental concepts and algorithms of reinforcement learning.
Practical guide to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for readers who want to learn how to apply machine learning algorithms to real-world problems.
Save
Provides a comprehensive overview of the potential risks and benefits of superintelligence. It valuable resource for readers who want to understand the potential implications of AI for the future of humanity.
Save
Provides a thought-provoking exploration of the possible futures of humanity in the age of AI. It valuable resource for readers who want to think about the long-term implications of AI and other emerging technologies.
Practical guide to data analysis using Python. It covers a wide range of topics from data cleaning to data visualization. It valuable resource for readers who want to learn how to use Python for data analysis.
Save
Practical guide to data science using R. It covers a wide range of topics from data cleaning to data visualization. It valuable resource for readers who want to learn how to use R for data science.
Practical guide to data science for business. It covers a wide range of topics from data collection to data mining. It valuable resource for readers who want to learn how to use data science to solve business problems.
Save
Provides a thought-provoking exploration of the possible futures of humanity. It valuable resource for readers who want to think about the long-term implications of AI and other emerging technologies.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m1l1nu/introduction
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Effort
7.5 total hours
Via
Udemy
Instructor
Damien Benveniste
Language
English
Good to know
Taught by an expert in the field of natural language processing
Covers a wide range of topics related to large language models
Hands-on and interactive, allowing learners to gain practical experience
Provides a strong foundation for beginners in the field of large language models
Requires some prior knowledge of machine learning and programming
May require additional costs for using OpenAI models
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