Offered By: IBMSkillsNetwork
AI Application Project with RAG and LangChain
Build a real-world Generative AI app using LangChain, RAG, and vector databases. Gain hands-on skills to boost your AI career and impress in interviews.
Continue reading Premium
Course
Artificial Intelligence
5.0
At a Glance
Build a real-world Generative AI app using LangChain, RAG, and vector databases. Gain hands-on skills to boost your AI career and impress in interviews.
By completing the AI Application Project with RAG and LangChain course, you'll gain the expertise to build a fully functional Generative AI application, setting you apart in job interviews and real-world projects.
You will learn:
- Explore building real-world Generative AI applications
- Master LangChain for document processing and improve model performance through text splitting techniques
- Gain valuable experience configuring vector databases to efficiently manage and retrieve document embeddings
- Build an interactive Gradio interface and a QA bot, equipping you with practical skills in AI-driven customer support
Course Overview
This course is designed to prepare learners to meet the soaring demand for Generative AI applications by helping learners build their own Generative AI application, ideal for those looking to enhance their career prospects.
Learners will develop key skills such as using LangChain for document loading and text-splitting strategies, along with applying new skills to upload documents from various sources. You will also learn to usewatsonx to embed documents and LangChain to fetch documents.
Additionally, you will implement RAG to enhance model responsiveness, and set up vector databases for storing document embeddings. You’ll also build a QA bot using LangChain and LLMs to answer questions from the loaded documents.
By the end of the intermediate-level course, you’ll have practical experience through hands-on labs and projects. If you’re ready to advance your career with real-world AI experience, enroll today!
Course Syllabus
Module 0: Welcome
- Video: Course Introduction
- General Information
- Reading: Learning Objectives and Syllabus
- Grading Scheme
- Specialization Overview
- Helpful Tips for Course Completion
Module 1: Document Loader Using LangChain
- Module 1: Introduction and Learning Objectives
- Video: Load Your Document from Different Sources
- Lab: Load Documents Using LangChain for Different Sources
- Lab: Put Whole Document into Prompt and Ask the Model
- Video: Strategies for Splitting Text for Optimal Processing
- Lab: Apply Text Splitting Techniques to Enhance Model Responsiveness
- Practice Quiz: Document Loader Using LangChain
- Reading: Summary and Highlights
Module 2: RAG Using LangChain
- Module Introduction and Learning Objectives
- Reading: Embed Documents Using Watsonx’s Embedding Model
- Lab: Embed Documents Using Watsonx’s Embedding Model
- Video: Introduction to Vector Databases for Storing Embeddings
- Lab: Create and Configure a Vector Database to Store Document Embeddings
- Video: Explore Advanced Retrievers in LangChain: Part 1
- Video: Explore Advanced Retrievers in LangChain: Part 2
- Lab: Develop a Retriever to Fetch Document Segments Based on Queries
- Reading: Compare Fine-Tuning Using InstructLab with RAG
- Practice Quiz: RAG Using LangChain
- Module Summary: RAG Using LangChain
Module 3: Create a QA Bot to Read Your Document
- Module Introduction and Learning objectives
- Video: Getting Started with Gradio
- Lab: Set Up a Simple Gradio Interface to Interact with Your Models
- Reading: Project Overview
- Reading: Construct a QA Bot That Leverages the LangChain and LLM to Answer Questions from Loaded Document
- Lab: Construct a QA Bot That Leverages the LangChain and LLM to Answer Questions from Loaded Document
- Practice Quiz: Create a QA Bot to Read Your Document
- Module Summary: Create a QA Bot to Read Your Document
- Final Project: Build an AI RAG Assistant Using LangChain
- Cheat Sheet: Project: Generative AI Applications with RAG and LangChain
- Course Glossary: Project: Generative AI Applications with RAG and LangChain
- Course Graded Quiz: AI Application Project with RAG and LangChain
Recommended Skills Prior to Taking this Course
This course requires a basic knowledge of Python.
Estimated Effort
9 Hours
Level
Intermediate
Skills You Will Learn
Generative AI Applications, Gradio, LangChain, Retrieval Augmented Generation (RAG), Vector Database
Language
English
Course Code
AI0214EN