This course will allow you to apply your acquired knowledge and skills to a capstone project. You’ll learn about document loaders from LangChain and then use that knowledge to load your document from various sources. Then, you’ll learn about text-splitting strategies and apply them to enhance model responsiveness. You’ll then use Watsonx to embed documents, a vector database to store document embeddings, and LangChain to develop a retriever to fetch documents. Further, you’ll implement RAG to improve retrieval, create a QA bot, and set up a simple Gradio interface to interact with your models. Finally, you will construct a QA bot to answer questions from loaded documents.

By the end of the course, you will be able to:

Document Loader Using LangChain

RAG Using LangChain

Create a QA Bot to Read Your Document