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.
Project: Generative AI Applications with RAG and LangChain
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Project: Generative AI Applications with RAG and LangChain
This course is part of multiple programs.
Instructors: Kang Wang
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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.
Skills you'll gain
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Recently updated!
October 2024
Assessments
7 assignments
Taught in English
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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
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Frequently asked questions
This course is suitable for those interested in AI engineering and includes training, developing, fine-tuning, and deploying large language models (LLMs). It is the ideal project course for learners who have completed the other courses in the Specialization title: Generative AI Engineering with LLMs.
Existing and aspiring data scientists, AI engineers, and machine learning engineers will benefit greatly from completing this project.
With 3–4 hours of study per week, you can complete this course and the guided project in 3 weeks. If you are able to put in more time per week, you can complete it a lot faster!
This course is intermediate level, so you must have basic knowledge of Python. Familiarity with LLMs, LangChain, and RAG would be an added advantage.However, to get the most out of this course, we recommend that you complete all the other courses in the IBM Generative AI Engineering with LLMs specialization.