Welcome to the LangChain AI JavaScript course.
As we stand here in It's not just a buzzword - it's a reality shaping industries, from finance to healthcare, logistics, and entertainment. And you, as a developer, are in a prime position to ride the wave.
Dive into the innovative world of building Large Language Models (LLM) with LangChain, a groundbreaking tool for rapidly building AI powered applications.
Read more
Welcome to the LangChain AI JavaScript course.
As we stand here in It's not just a buzzword - it's a reality shaping industries, from finance to healthcare, logistics, and entertainment. And you, as a developer, are in a prime position to ride the wave.
Dive into the innovative world of building Large Language Models (LLM) with LangChain, a groundbreaking tool for rapidly building AI powered applications.
Welcome to the LangChain AI JavaScript course.
As we stand here in It's not just a buzzword - it's a reality shaping industries, from finance to healthcare, logistics, and entertainment. And you, as a developer, are in a prime position to ride the wave.
Dive into the innovative world of building Large Language Models (LLM) with LangChain, a groundbreaking tool for rapidly building AI powered applications.
In this course, we place an equal emphasis on theory and practice. We’ll start easy, and increase the complexity with each project. Additionally, you'll have starter templates for each project, so that you spend most of your time learning about AI, and less time worrying about setting up your code environment.
In 2023 alone, we’ve seen a rise in AI products making hundreds to thousands of dollars a month built by solo developers. At the end of the course, you will have built SIX stunning full-stack AI applications that will give you the skills to build your own AI products from scratch.
The world is moving fast, and AI is leading the charge. Don't be left behind, wishing you had started learning AI sooner. So secure your spot in the course, and take the first step towards your AI journey today.
In this course, we use cutting edge technologies to create a seamless developer experience:
LangChain, an AI framework for rapidly building LLM applications with OpenAI, HuggingFace, and more.
NextJS - Allows you to build the frontend and backend in one language, JavaScript.
Vercel - You'll know how to deploy your LLM application to users, without pulling your hair out.
TailwindCSS - Each project comes with a pre-styled template, so you don't need to worry about styling.
By the end of the course you will the skills to:
Chat With Anything - use AI to chat with books, videos, etc.
Supercharge Your Work - create autonomous AI agents that can "think" through complex tasks
Launch Your Product - display your AI projects for potential employers/customers to see
Troubleshoot Anything - how to use AI tools beyond ChatGPT to become the "10x developer"
Course Pre-Requisites:
Some experience in JavaScript (or a similar programming language) is helpful
Some experience in React is helpful
No experience in AI, NextJS, TailwindCSS necessary
A desire to build your own AI applications.
This is not just a course, it's a community. Along with lifetime access to the course, you'll get:
Dedicated 1 on 1 troubleshooting support with me
Notion Course Textbook with additional AI resources, FAQ, troubleshooting guides
Access to an exclusive Discord community to connect with other learners
No extra cost for continuous updates and improvements to the course
Here's a deal for you
UDEAFNULP2024
What's inside
Learning objectives
- Chat with anything - use ai to chat with books, videos, etc.
- Supercharge your work - create autonomous ai agents that can "think" through complex tasks
- Launch your product - display your ai projects for potential employers/customers to see
- Troubleshoot anything - how to use ai tools beyond chatgpt to become the "10x developer"
- Chat with anything - use ai to chat with books, videos, etc.
- Supercharge your work - create autonomous ai agents that can "think" through complex tasks
- Launch your product - display your ai projects for potential employers/customers to see
- Troubleshoot anything - how to use ai tools beyond chatgpt to become the "10x developer"
Syllabus
Set up environment and start writing your first lines of LangChain!
1.1 • Developer Environment Setup
Note: Node Version
1.2 Cloning the Repository
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Syllabus
The most helpful resource here is the LangChain KapaAI.
You can ask your questions to an AI assistant that is trained on AI!
This step is crucial so that we'll have API keys in the NodeJS runtime environment.
NOTE: To skip this section, simply copy the app/memory/page-finished.jsx
LangChain provides a streaming API that allows you to get words streamed back to you as they are generated. This is useful for chatbots, where you want to show the user what is being generated as it is being generated. You can find an example of how to use the streaming API in the LangChain documentation.
The VectorDBQAChain is a tool that allows you to interact with a vector store in an agentic manner. It is created by combining an LLM with a vector store, and can be used to answer questions about the data in the vector store. To use the VectorDBQAChain, you first need to create a vector store with your data, and then create the chain using the OpenAI LLM and the vector store. Once you have the chain, you can create a tool to use it, and then use the tool just like any other tool.
ChatOpenAI is a chat model provided by LangChain. It allows you to get chat completions by passing one or more messages to the chat model. The response will also be a message. The types of messages currently supported in LangChain are AIChatMessage, HumanChatMessage, SystemChatMessage, and a generic ChatMessage. Most of the time, you'll just be dealing with HumanChatMessage, AIChatMessage, and SystemChatMessage. You can also use the streaming API to get words streamed back to you as they are generated. This is useful for chatbots, where you want to show the user what is being generated as it is being generated.
HNSWLib is an in-memory vector store that can be saved to a file and is only available on Node.js. It is used for similarity search and uses HNSWLib. You can create a new index from texts or a loader, save an index to a file and load it again, and filter documents.
The Web Browser Tool allows an agent to visit a website and extract information. It has two modes of operation:
When called with only a URL, it produces a summary of the website contents.
When called with a URL and a description of what to find, it uses an in-memory Vector Store to find the most relevant snippets and summarize those.
To use the Web Browser Tool, you need to install the dependencies cheerio and axios. The tool can be used standalone or in an agent.
The "zero-shot-react-description" agent is a type of agent that can be used with text LLMs. It is a stateless wrapper around an agent prompt chain, such as MRKL, that formats tools into the prompt and parses responses obtained from the chat model. This agent is recommended for use with small tasks.
The SummaryChain is a chain provided by LangChain that can be used to generate a summary of a given input text. To use it, you can import it from langchain/chains and create an instance of it. Once you have created the chain, you can call its call method with a text input to get a summary of the text.
Good to know
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Activities
Review Linear Algebra Concepts
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Refreshes essential linear algebra knowledge for understanding the mathematical foundations of AI.
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Linear Algebra
Show steps
-
Review the basics of vectors, matrices, and linear transformations.
-
Practice solving systems of linear equations.
-
Read articles or watch videos on linear algebra applications in AI.
Read Deep Learning with Python
Show steps
Provides a solid foundation in deep learning concepts and techniques, which are essential for building language models in this course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read Chapters 1-4 to gain an understanding of the basics of deep learning.
-
Work through the hands-on exercises in Chapters 5-7 to apply your knowledge.
-
Complete the review questions at the end of each chapter to test your understanding.
Build a Chatbot with Hugging Face
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Provides hands-on experience building a chatbot using Hugging Face, a popular library for natural language processing.
Browse courses on
Chatbot
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-
Follow the Hugging Face tutorial on building a chatbot.
-
Experiment with different models and parameters to customize your chatbot.
-
Deploy your chatbot to a platform like Dialogflow or Rasa.
Four other activities
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Show all seven activities
Practice Prompt Engineering
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Develops proficiency in crafting effective prompts for language models, which is crucial for getting the most out of AI applications.
Browse courses on
Prompt Engineering
Show steps
-
Use the OpenAI Playground or a similar tool to experiment with different prompts.
-
Analyze the responses from the language model to identify patterns and improve your prompts.
-
Read articles and tutorials on best practices for prompt engineering.
Mentor a Beginner in AI
Show steps
Reinforces your understanding of AI concepts and helps others in their learning journey.
Show steps
-
Identify a beginner who is interested in learning about AI.
-
Meet regularly to discuss AI concepts and answer questions.
-
Provide feedback on their projects and help them troubleshoot.
Build a Personal Recommendation System
Show steps
Applies the concepts of natural language processing and machine learning to create a personalized recommendation system tailored to your interests.
Show steps
-
Gather a dataset of items and user preferences.
-
Train a language model on the dataset to learn the relationships between items and user preferences.
-
Build a user interface to allow users to interact with the recommendation system.
Contribute to a LangChain Project
Show steps
Provides a practical experience in working with AI tools and technologies, while contributing to the open source community.
Browse courses on
LangChain
Show steps
-
Find an open-source LangChain project that interests you.
-
Identify an area where you can contribute, such as adding features or improving documentation.
-
Submit a pull request with your changes to the project.
Review Linear Algebra Concepts
Show steps
Refreshes essential linear algebra knowledge for understanding the mathematical foundations of AI.
Browse courses on
Linear Algebra
Show steps
- Review the basics of vectors, matrices, and linear transformations.
- Practice solving systems of linear equations.
- Read articles or watch videos on linear algebra applications in AI.
Read Deep Learning with Python
Show steps
Provides a solid foundation in deep learning concepts and techniques, which are essential for building language models in this course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
- Read Chapters 1-4 to gain an understanding of the basics of deep learning.
- Work through the hands-on exercises in Chapters 5-7 to apply your knowledge.
- Complete the review questions at the end of each chapter to test your understanding.
Build a Chatbot with Hugging Face
Show steps
Provides hands-on experience building a chatbot using Hugging Face, a popular library for natural language processing.
Browse courses on
Chatbot
Show steps
- Follow the Hugging Face tutorial on building a chatbot.
- Experiment with different models and parameters to customize your chatbot.
- Deploy your chatbot to a platform like Dialogflow or Rasa.
Practice Prompt Engineering
Show steps
Develops proficiency in crafting effective prompts for language models, which is crucial for getting the most out of AI applications.
Browse courses on
Prompt Engineering
Show steps
- Use the OpenAI Playground or a similar tool to experiment with different prompts.
- Analyze the responses from the language model to identify patterns and improve your prompts.
- Read articles and tutorials on best practices for prompt engineering.
Mentor a Beginner in AI
Show steps
Reinforces your understanding of AI concepts and helps others in their learning journey.
Show steps
- Identify a beginner who is interested in learning about AI.
- Meet regularly to discuss AI concepts and answer questions.
- Provide feedback on their projects and help them troubleshoot.
Build a Personal Recommendation System
Show steps
Applies the concepts of natural language processing and machine learning to create a personalized recommendation system tailored to your interests.
Show steps
- Gather a dataset of items and user preferences.
- Train a language model on the dataset to learn the relationships between items and user preferences.
- Build a user interface to allow users to interact with the recommendation system.
Contribute to a LangChain Project
Show steps
Provides a practical experience in working with AI tools and technologies, while contributing to the open source community.
Browse courses on
LangChain
Show steps
- Find an open-source LangChain project that interests you.
- Identify an area where you can contribute, such as adding features or improving documentation.
- Submit a pull request with your changes to the project.
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