Project: Generative AI Applications with RAG and LangChain

Project: Generative AI Applications with RAG and LangChain

This course is part of multiple programs.

Instructors: Kang Wang

What you'll learn

Gain practical experience building your own real-world gen AI application that you can talk about in interviews.
Get hands-on using LangChain to load documents and apply text splitting techniques with RAG and LangChain to enhance model responsiveness.
Create and configure a vector database to store document embeddings and develop a retriever to fetch document segments based on queries.
Set up a simple Gradio interface for model interaction and construct a QA bot using LangChain and an LLM to answer questions from loaded documents.

Details to know

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Assessments

7 assignments

Taught in English

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There are 3 modules in this course

Get ready to put all your gen AI engineering skills into practice! This guided project will test and apply the knowledge and understanding you’ve gained throughout the previous courses in the program. You will build your own real-world gen AI application.

During this course, you will fill the final gaps in your knowledge to extend your understanding of document loaders from LangChain. You will then apply your new skills to uploading your own documents from various sources. Next, you will look at text-splitting strategies and use them to enhance model responsiveness. Then, you will use watsonx to embed documents, a vector database to store document embeddings, and LangChain to develop a retriever to fetch documents. As you work through your project, you will also implement RAG to improve retrieval, create a QA bot, and set up a simple Gradio interface to interact with your models. By the end of the course, you will have a hands-on project that provides engaging evidence of your generative AI engineering skills that you can talk about in interviews. If you’re ready to add some real-world experience to your portfolio, enroll today and fuel your AI engineering career.

In this module, you will learn all about document loaders from LangChain and then use that knowledge to load your document from various sources. You will also explore the various text splitting strategies with RAG and LangChain and apply them to enhance model responsiveness. Hands-on labs will provide you an opportunity to practice loading documents as well as implement the text-splitting techniques you have learned.

What's included

3 videos4 readings2 assignments3 app items1 plugin

3 videosTotal 16 minutes

Course Introduction 3 minutesPreview module
Load Your Document from Different Sources 6 minutes
Strategies for Splitting Text for Optimal Processing 6 minutes

4 readingsTotal 14 minutes

Course Overview 5 minutes
Specialization Overview 5 minutes
Best Practices for Loading Documents in LangChain Applications 3 minutes
Reading: Summary and Highlights 1 minute

2 assignmentsTotal 18 minutes

Practice Quiz: Different Document Loaders from LangChain9 minutes
Practice Quiz: Text Splitter9 minutes

3 app itemsTotal 110 minutes

Lab: Load Documents Using LangChain for Different Sources60 minutes
Lab: Put Whole Document into Prompt and Ask the Model20 minutes
Lab: Apply Text Splitting Techniques to Enhance Model Responsiveness30 minutes

1 pluginTotal 1 minute

Helpful Tips for Course Completion1 minute

In this module, you will learn how to store embeddings using a vector store and how to use Chroma DB to save embeddings. You’ll gain insights into LangChain retrievers like the Vector Store-Based, Multi-Query, Self-Query, and Parent Document Retriever. In hands-on labs, you’ll prepare and preprocess documents for embedding and use watsonx.ai to generate embeddings for your documents. You’ll use vector databases such as Chroma DB and FAISS to store embeddings generated from textual data using LangChain. Finally, you’ll use various retrievers to efficiently extract relevant document segments from text using LangChain.

What's included

3 videos1 reading2 assignments3 app items2 plugins

3 videosTotal 12 minutes

Introduction to Vector Databases for Storing Embeddings 4 minutesPreview module
Explore Advanced Retrievers in Langchain: Part 12 minutes
Explore Advanced Retrievers in Langchain - Part 25 minutes

1 readingTotal 2 minutes

Module Summary: RAG Using LangChain 2 minutes

2 assignmentsTotal 18 minutes

Practice Quiz: Embedding the Document9 minutes
Practice Quiz: Retriever9 minutes

3 app itemsTotal 100 minutes

Lab: Embed Documents using watsonx’s Embedding Model30 minutes
Lab: Create and Configure a Vector Database to Store Document Embeddings30 minutes
Lab: Develop a Retriever to Fetch Document Segments Based on Queries40 minutes

2 pluginsTotal 25 minutes

Embed Documents Using watsonx’s Embedding Model10 minutes
Reading: Compare Fine-Tuning Using InstructLab with RAG15 minutes

In this module, you will learn how to implement RAG to improve retrieval. You will become familiar with Gradio and how to set up a simple Gradio interface to interact with your models. You will also learn how to construct a QA bot to answer questions from loaded documents using LangChain and LLMs. Using hands-on labs, you will have the opportunity to practice setting up a Gradio interface, as well as constructing a QA bot. In the final project, you will build an AI application using RAG and LangChain.

What's included

1 video4 readings3 assignments1 peer review2 app items4 plugins

1 videoTotal 3 minutes

Getting Started with Gradio 3 minutesPreview module

4 readingsTotal 8 minutes

Module Summary: Create a QA Bot to Read Your Document 1 minute
Course Conclusion 3 minutes
Congratulations and Next Steps2 minutes
Thanks from the Course Team2 minutes

3 assignmentsTotal 48 minutes

Practice Quiz: Introduction to Gradio9 minutes
Practice Quiz: Build a QA Bot Web App9 minutes
Course Graded Quiz: Project: Generative AI Applications with RAG and LangChain30 minutes

1 peer reviewTotal 60 minutes

Final Project: Build an AI RAG Assistant Using LangChain60 minutes

2 app itemsTotal 60 minutes

Lab: Set Up a Simple Gradio Interface to Interact with Your Models30 minutes
Lab: Construct a QA Bot that Leverages the LangChain and LLM to Answer Questions from Loaded Document30 minutes

4 pluginsTotal 47 minutes

Reading: Project Overview2 minutes
Reading: Construct a QA Bot that Leverages the LangChain and LLM to answer questions from loaded document15 minutes
Cheat Sheet: Project: Generative AI Applications with RAG and LangChain15 minutes
Course Glossary: Project: Generative AI Applications with RAG and LangChain15 minutes