In this module, you will learn how to embed documents using watsonx’s embedding model and store these embeddings using vector databases, such as Chroma DB and FAISS. You will explore the role of embeddings in RAG pipelines, configure vector stores to manage these embeddings, and use LangChain to preprocess documents for embedding. Additionally, you will gain hands-on experience with advanced retrievers in LangChain, such as Vector Store-Based, Multi-Query, Self-Query, and Parent Document retrievers, to extract relevant information from documents efficiently. Finally, you’ll compare RAG-based approaches with fine-tuning using InstructLab to evaluate their trade-offs and applicability.

In this module, you will combine all the components you’ve learned to build a complete generative AI application using LangChain and RAG. You’ll learn how to implement RAG to improve information retrieval, set up user interfaces using Gradio, and construct a question-answering bot that leverages LLMs and LangChain to respond to queries from loaded documents. Through hands-on labs, you’ll practice building a Gradio interface and developing your own QA bot. In the final project, you will build an AI application using RAG and LangChain. The supporting materials, like a cheat sheet and glossary, will reinforce your understanding, build confidence in your implementation skills, and assess your learning through a graded quiz. You'll leave this module with a deployable AI-powered assistant and clear the next steps for advancing your skills.