IBM
Project: Generative AI Applications with RAG and LangChain
IBM

Project: Generative AI Applications with RAG and LangChain

Kang Wang
Wojciech 'Victor' Fulmyk

Instructors: Kang Wang

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace

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.

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Recently updated!

October 2024

Assessments

7 assignments

Taught in English

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This course is part of the Generative AI Engineering with LLMs Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 3 modules in this course

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 videos3 readings2 assignments3 app items1 plugin

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

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

Instructors

Kang Wang
3 Courses1,907 learners

Offered by

IBM

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