Generative AI - LangChain, RAG for javascript developers

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1 - Introduction

1 -Welcome.mp4

02:42

2 -Course Structure.mp4

03:24

2 -discord community for genai javascript developers.zip

2 -youtube channel.zip

3 -Introduction to Generative AI.mp4

06:07

2 - Getting Started

1 -Installing Nodejs.mp4

02:43

2 -Project Setup.mp4

03:22

2 -github repository.zip

3 -Create API Key.mp4

03:17

3 -openai login.zip

4 -Your First Generative AI Program.mp4

03:29

3 - Basic LLM Chain

1 -About this section.mp4

00:54

1 -discord channel.zip

2 -Introduction to Langchain.mp4

04:13

2 -langchain js documentation.zip

3 -Prompt Template.mp4

03:42

4 -LLM and Output parsers.mp4

02:46

5 -Langchain Framework.mp4

04:07

6 -LLM Chain - Legacy.mp4

03:55

7 -LLM Chain - LCEL.mp4

06:48

8 -LLM Parameters - temperature, Top p, Top k.mp4

08:53

9 -LLM Parameters - continue.mp4

10:25

10 -LLM models.mp4

02:40

11 -Logging and Debugging Langchain Apps.mp4

07:01

4 - LLM Providers

1 -About this section.mp4

01:25

2 -AWS Bedrock.mp4

12:41

3 -Anthropic.mp4

09:39

5 - RAG - Q&A with documents

1 -About this section.mp4

01:14

2 -What is RAG.mp4

04:12

3 -Vector Embeddings.mp4

11:17

4 -RAG Architecture.mp4

03:06

5 -Building RAG - load documents.mp4

06:54

6 -Building RAG - chunking.mp4

12:00

7 -Building RAG - vectorization.mp4

07:26

8 -Building RAG - retrieval.mp4

05:00

9 -Building RAG - augmentation.mp4

03:39

10 -Building RAG - LLM and output parser.mp4

02:36

11 -Building RAG - Chain.mp4

04:56

12 -Building RAG - Execute.mp4

07:46

6 - Chat history

1 -About this section.mp4

00:56

2 -Why chat history.mp4

01:45

3 -Chat history - concept.mp4

04:16

4 -Chat history - coding.mp4

11:08

7 - Congratulations

1 -Congratulations on completion.mp4

00:58

1 -youtube channel.zip

Description

Learn Generative AI and Langchain by building real life use cases using Javascript

What You'll Learn?

Generative AI technology and building LLM powered applications
Langchain with Javascript/Typescript using latest Langchain version
Build RAG application using private data
Build chatbot application using LLMs
Working with different LLMs and LLM providers
Langchain: LCEL, chains, retrievers, splitters, output parsers, chat memory, langsmith
Solid understand of concepts: embeddings, vector databases, LLM parameters

Who is this for?

Javascript developers and architects looking to advance their career with Generative AI technology

Engineers that want to learn how to build Generative AI based applications with LangChain

What You Need to Know?

Basic JavaScript/Typescript knowledge, experience with Nodejs

Basic software engineering concepts

No machine learning experience is needed

More details

Description
Welcome to the Generative AI and LangChain Course for JavaScript Developers! This course is tailored specifically for JavaScript professionals ready to advance their careers in the rapidly growing field of generative AI. While AI and machine learning have traditionally been dominated by Python, generative AI has opened up new possibilities, allowing JavaScript developers to build high-quality, LLMÂ powered applications.
Who Should Take This Course? This course is designed for developers and architects with JavaScript and Node.js experience who are eager to build applications powered by large language models (LLMs). Youâ€™ll learn how to use JavaScript with LangChain to create generative AI applications, mastering core concepts like RAG (retrieval-augmented generation), embeddings, vector databases, and more. By the end, youâ€™ll be equipped to develop robust generative AI applications.
Course Journey: We start with setting up the development environment, creating basic applications to explore key frameworks. Then, weâ€™ll dive into advanced topics, building real-world applications with features like retrievable augmented generation and adding conversational layers with chat history.
Key Topics Covered:
LangChain with JavaScript/TypeScript
LLMs: Working with top providers like AWS Bedrock, GPT, and Anthropic
Prompts & PromptTemplates
Output Parsers
Chains: Including legacy chains and LCEL
LLM Parameters: Temp, Top-p, Top-k
LangSmith
Embeddings & VectorStores (e.g., Pinecone)
RAG (Retrieval Augmentation Generation)
Tools: Web crawlers, document loaders, text splitters
Memory & Chat History
Throughout the course, youâ€™ll engage in hands-on exercises and build real-world projects to reinforce each concept, ensuring a solid foundation in generative AI with JavaScript. By course completion, youâ€™ll be proficient in using LangChain to develop versatile, high-performance LLM applications.
Whatâ€™s Included? This course is also a community experience. With lifetime access, youâ€™ll receive:
GitHub repositories with complete course code
Access to an exclusive Discord community for support and discussion on GenAIÂ topics
Free updates and continuous improvements at no extra cost
Disclaimers:
This is not a beginner course; software engineering experience and some experience in JavaScript are assumed.
We will be using the VSCode IDE (though any editor is welcome).
Some LLM services may require payment, but weâ€™ll utilize free options whenever possible.
The views and opinions expressed here are my own and do not represent those of my employer.
Who this course is for:
Javascript developers and architects looking to advance their career with Generative AI technology
Engineers that want to learn how to build Generative AI based applications with LangChain

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