English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 34m | 202 MB
AI is becoming more and more of a requirement for JavaScript development projects. You can use AI technologies for UI enhancements, as well as built upon several AIs with JavaScript. As we move toward a world where more AI programming is normalized, the JavaScript ecosystem contains tools to make your work as a developer easier. In this course, software developer and instructor Eve Porcello surveys the options available and shows you how to build chat and text analysis programs that use JavaScript libraries in Node.js.
After taking the course, you will be able to analyze text and build chat applications that have a structured output. You will also learn how to save results to databases that are familiar to many JavaScript developers.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.
Table of Contents
Introduction
1 Learn to build AI-enhanced applications
2 Understanding AI in the JavaScript ecosystem
3 What you should know
Creating the App
4 Installing OpenAI with npm
5 Generating an API key
6 Choosing and setting a language model
Working with Prompts
7 Writing functions
8 Working with streams
9 Creating a prompt
10 Refactoring functions with code prompts
11 Setting templates
12 Challenge Customizing questions
13 Solution Customizing questions
Incorporating APIs
14 Accepting user input with readline
15 Creating image descriptions based on a URL
16 Transcribing audio files with OpenAI
17 Building a Node app to transcribe audio
18 Using DALL-E 3 for image generation
Customizing the Assistants API
19 Using the Assistants API playground
20 Creating an assistant in a Node application
21 Adding messages to threads
22 Running and parsing the assistant response
Utilizing LangChain Features
23 Setting up a LangChain project
24 Creating a chat template
25 Using runnable sequences
26 Combining multiple chains
Conclusion
27 Next steps
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